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on Forecasting |
| By: | Yousef Adeli Sadabad; Mohammad Reza Hesamzadeh; Gyorgy Dan; Matin Bagherpour; Darryl R. Biggar |
| Abstract: | The System Price (SP) of the Nordic electricity market serves as a key reference for financial hedge contracts such as Electricity Price Area Differentials (EPADs) and other risk management instruments. Therefore, the identification of drivers and the accurate forecasting of SP are essential for market participants to design effective hedging strategies. This paper develops a systematic framework that combines interpretable drivers analysis with robust forecasting methods. It proposes an interpretable feature engineering algorithm to identify the main drivers of the Nordic SP based on a novel combination of K-means clustering, Multiple Seasonal-Trend Decomposition (MSTD), and Seasonal Autoregressive Integrated Moving Average (SARIMA) model. Then, it applies principal component analysis (PCA) to the identified data matrix, which is adapted to the downstream task of price forecasting to mitigate the issue of imperfect multicollinearity in the data. Finally, we propose a multi-forecast selection-shrinkage algorithm for Nordic SP forecasting, which selects a subset of complementary forecast models based on their bias-variance tradeoff at the ensemble level and then computes the optimal weights for the retained forecast models to minimize the error variance of the combined forecast. Using historical data from the Nordic electricity market, we demonstrate that the proposed approach outperforms individual input models uniformly, robustly, and significantly, while maintaining a comparable computational cost. Notably, our systematic framework produces superior results using simple input models, outperforming the state-of-the-art Temporal Fusion Transformer (TFT). Furthermore, we show that our approach also exceeds the performance of several well-established practical forecast combination methods. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.18887 |
| By: | Ava C. Blake; Nivika A. Gandhi; Anurag R. Jakkula |
| Abstract: | Accurate prediction of financial market volatility is critical for risk management, derivatives pricing, and investment strategy. In this study, we propose a multitude of regime-switching methods to improve the prediction of S&P 500 volatility by capturing structural changes in the market across time. We use eleven years of SPX data, from May 1st, 2014 to May 27th, 2025, to compute daily realized volatility (RV) from 5-minute intraday log returns, adjusted for irregular trading days. To enhance forecast accuracy, we engineered features to capture both historical dynamics and forward-looking market sentiment across regimes. The regime-switching methods include a soft Markov switching algorithm to estimate soft-regime probabilities, a distributional spectral clustering method that uses XGBoost to assign clusters at prediction time, and a coefficient-based soft regime algorithm that extracts HAR coefficients from time segments segmented through the Mood test and clusters through Bayesian GMM for soft regime weights, using XGBoost to predict regime probabilities. Models were evaluated across three time periods--before, during, and after the COVID-19 pandemic. The coefficient-based clustering algorithm outperformed all other models, including the baseline autoregressive model, during all time periods. Additionally, each model was evaluated on its recursive forecasting performance for 5- and 10-day horizons during each time period. The findings of this study demonstrate the value of regime-aware modeling frameworks and soft clustering approaches in improving volatility forecasting, especially during periods of heightened uncertainty and structural change. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.03236 |
| By: | Isaac Baley; Javier Turen |
| Abstract: | Professional forecasters adjust their inflation forecasts in a distinctly lumpy pattern, making infrequent but substantial revisions. Strategic concerns play a significant role - forecasters are more likely to adjust, and by larger amounts, when their forecasts deviate from the consensus. Using a fixed-event forecasting framework, we document the impact of lumpiness and consensus pressure on forecast adjustments. Our quantitative model, which integrates Bayesian belief updating with forecast revision costs and strategic concerns, not only replicates the observed lumpiness in survey data but also sheds light on forecasters' apparent overreactions to new information. This structured framework enables us to "cleanse" forecasts, isolating the underlying inflation beliefs that drive these forecasts. |
| Keywords: | forecasting, survey of professional forecasters, overreaction, consensus, forecast revision costs, forecast stability, strategic concerns, Bayesian learning, fixed-event forecasts |
| JEL: | D80 D81 D83 D84 E20 E30 |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:upf:upfgen:1898 |
| By: | Sid Ghatak; Arman Khaledian; Navid Parvini; Nariman Khaledian |
| Abstract: | There are inefficiencies in financial markets, with unexploited patterns in price, volume, and cross-sectional relationships. While many approaches use large-scale transformers, we take a domain-focused path: feed-forward and recurrent networks with curated features to capture subtle regularities in noisy financial data. This smaller-footprint design is computationally lean and reliable under low signal-to-noise, crucial for daily production at scale. At Increase Alpha, we built a deep-learning framework that maps over 800 U.S. equities into daily directional signals with minimal computational overhead. The purpose of this paper is twofold. First, we outline the general overview of the predictive model without disclosing its core underlying concepts. Second, we evaluate its real-time performance through transparent, industry standard metrics. Forecast accuracy is benchmarked against both naive baselines and macro indicators. The performance outcomes are summarized via cumulative returns, annualized Sharpe ratio, and maximum drawdown. The best portfolio combination using our signals provides a low-risk, continuous stream of returns with a Sharpe ratio of more than 2.5, maximum drawdown of around 3\%, and a near-zero correlation with the S\&P 500 market benchmark. We also compare the model's performance through different market regimes, such as the recent volatile movements of the US equity market in the beginning of 2025. Our analysis showcases the robustness of the model and significantly stable performance during these volatile periods. Collectively, these findings show that market inefficiencies can be systematically harvested with modest computational overhead if the right variables are considered. This report will emphasize the potential of traditional deep learning frameworks for generating an AI-driven edge in the financial market. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.16707 |
| By: | Lokesh Antony Kadiyala; Amir Mirzaeinia |
| Abstract: | The stock market is extremely difficult to predict in the short term due to high market volatility, changes caused by news, and the non-linear nature of the financial time series. This research proposes a novel framework for improving minute-level prediction accuracy using semantic sentiment scores from top ten different large language models (LLMs) combined with minute interval intraday stock price data. We systematically constructed a time-aligned dataset of AAPL news articles and 1-minute Apple Inc. (AAPL) stock prices for the dates of April 4 to May 2, 2025. The sentiment analysis was achieved using the DeepSeek-V3, GPT variants, LLaMA, Claude, Gemini, Qwen, and Mistral models through their APIs. Each article obtained sentiment scores from all ten LLMs, which were scaled to a [0, 1] range and combined with prices and technical indicators like RSI, ROC, and Bollinger Band Width. Two state-of-the-art such as Reformer and Mamba were trained separately on the dataset using the sentiment scores produced by each LLM as input. Hyper parameters were optimized by means of Optuna and were evaluated through a 3-day evaluation period. Reformer had mean squared error (MSE) or the evaluation metrics, and it should be noted that Mamba performed not only faster but also better than Reformer for every LLM across the 10 LLMs tested. Mamba performed best with LLaMA 3.3--70B, with the lowest error of 0.137. While Reformer could capture broader trends within the data, the model appeared to over smooth sudden changes by the LLMs. This study highlights the potential of integrating LLM-based semantic analysis paired with efficient temporal modeling to enhance real-time financial forecasting. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.01203 |
| By: | Anoushka Harit; Zhongtian Sun; Jongmin Yu |
| Abstract: | We propose the Causal Sphere Hypergraph Transformer (CSHT), a novel architecture for interpretable financial time-series forecasting that unifies \emph{Granger-causal hypergraph structure}, \emph{Riemannian geometry}, and \emph{causally masked Transformer attention}. CSHT models the directional influence of financial news and sentiment on asset returns by extracting multivariate Granger-causal dependencies, which are encoded as directional hyperedges on the surface of a hypersphere. Attention is constrained via angular masks that preserve both temporal directionality and geometric consistency. Evaluated on S\&P 500 data from 2018 to 2023, including the 2020 COVID-19 shock, CSHT consistently outperforms baselines across return prediction, regime classification, and top-asset ranking tasks. By enforcing predictive causal structure and embedding variables in a Riemannian manifold, CSHT delivers both \emph{robust generalisation across market regimes} and \emph{transparent attribution pathways} from macroeconomic events to stock-level responses. These results suggest that CSHT is a principled and practical solution for trustworthy financial forecasting under uncertainty. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.04357 |
| By: | Monica Bonacina (University of Milan, Fondazione Eni Enrico Mattei, Bocconi University and University of Insubria); Romolo Consigna Tokong (Fondazione Eni Enrico Mattei and University of Florence) |
| Abstract: | The European Union’s decarbonisation strategy necessitates profound shifts across all sectors, with road transport presenting a particularly formidable challenge due to its sustained emissions growth since 1990. Given that Italy’s road transport sector is the third-largest consumer of fossil fuels in Europe, its role is pivotal in achieving these collective climate objectives. This study employs grey forecasting models to assess the projected contribution of alternative fuels – specifically biodiesel, bio-gasoline, biomethane, and electricity – to Italy’s 2030 decarbonisation pathway. The results suggest that consumption of these energy carriers will reach around 5 Mtoe (million tons of oil equivalent), representing a threefold increase compared to 2022 levels. While our analysis forecasts that biomethane, will entirely displace its fossil counterpart and that electricity consumption will expand considerably, the progress in the use of liquid biofuels could be lower than that reported in Italy’s National Energy and Climate Plan (NECP). According to grey models, in 2030, alternative fuels could meet one-sixth of the final energy demand for Italian road transport: a considerable improvement from current levels but less than the two-fifths share needed to align with the EU’s broader decarbonisation objectives. The findings suggest that the decarbonisation of road transport, largely attributed to the use of biofuels, is currently outpacing the progress achieved through the electrification of the vehicle fleet. This underscores the imperative of adopting a holistic strategy that leverages the full potential of all technologies. Such a unified design is essential to foster synergy and expedite the achievement of climate objectives in a manner that is both efficient and inclusive. |
| Keywords: | Road Transport, Final Energy Consumption, Alternative Fuels, Italy’s National Energy and Climate Plan, Grey Forecasting Models |
| JEL: | Q2 Q4 C22 C45 C53 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:fem:femwpa:2025.19 |
| By: | Giovanni Bonaccolto (Department of Economics and Law, ``Kore" University of Enna, Italy); Sayar Karmakar (Department of Statistics, University of Florida, USA); Elie Bouri (Adnan Kassar School of Business, Lebanese American University, Lebanon); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa) |
| Abstract: | Previous studies examine spillover effects across the volatility of several cryptocurrencies in the mean or across quantiles without addressing the issue of high dimensionality. Using a large dataset of 50 cryptocurrencies, we employ a LASSO-regularized Quantile VAR framework and show that spillover effects differ across low, medium, and high volatility regimes, especially when evaluated dynamically over time, with sharp increases around tail events such as the war in Ukraine. Importantly, we demonstrate that the LASSO-QVAR model delivers statistically significant forecasting improvements over its univariate counterpart, underscoring the role of interconnectedness in enhancing volatility prediction across cryptocurrencies. |
| Keywords: | Cryptocurrencies, Volatility, LASSO Quantile VAR, Spillovers; Forecasting |
| JEL: | C32 C53 G10 G17 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202538 |
| By: | Davit Gondauri |
| Abstract: | Inflation forecasting is a core socio-economic challenge in modern macroeconomic modeling, especially when cyclical, structural, and shock factors act simultaneously. Traditional systems such as FPAS and ARIMA often struggle with cyclical asymmetry and unexpected fluctuations. This study proposes a hybrid framework (FPAS + $\zeta$) that integrates a structural macro model (FPAS) with cyclical components derived from the Riemann zeta function $\zeta(1/2 + i t)$. Using Georgia's macro data (2005-2024), a nonlinear argument $t$ is constructed from core variables (e.g., GDP, M3, policy rate), and the hybrid forecast is calibrated by minimizing RMSE via a modulation coefficient $\alpha$. Fourier-based spectral analysis and a Hidden Markov Model (HMM) are employed for cycle/phase identification, and a multi-criteria AHP-TOPSIS scheme compares FPAS, FPAS + $\zeta$, and ARIMA. Results show lower RMSE and superior cyclical responsiveness for FPAS + $\zeta$, along with early-warning capability for shocks and regime shifts, indicating practical value for policy institutions. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.02966 |
| By: | Qingyuan Han |
| Abstract: | Equity markets have long been regarded as unpredictable, with intraday price movements treated as stochastic noise. This study challenges that view by introducing the Extended Samuelson Model (ESM), a natural science-based framework that captures the dynamic, causal processes underlying market behavior. ESM identifies peaks, troughs, and turning points across multiple timescales and demonstrates temporal compatibility: finer timeframes contain all signals of broader ones while offering sharper directional guidance. Beyond theory, ESM translates into practical trading strategies. During intraday sessions, it reliably anticipates short-term reversals and longer-term trends, even under the influence of breaking news. Its eight market states and six directional signals provide actionable guardrails for traders, enabling consistent profit opportunities. Notably, even during calm periods, ESM can capture 10-point swings in the S&P 500, equivalent to $500 per E-mini futures contract. These findings resonate with the state-based approaches attributed to Renaissance Technologies' Medallion Fund, which delivered extraordinary returns through systematic intraday trading. By bridging normal conditions with crisis dynamics, ESM not only advances the scientific understanding of market evolution but also provides a robust, actionable roadmap for profitable trading. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.01542 |
| By: | Ziyan Zhao (Economic Growth Centre, School of Social Sciences, Nanyang Technological University); Qingfeng Liu (Department of Industrial and Systems Engineering, Hosei University) |
| Abstract: | This study proposes a time-varying structural approximate dynamic factor (TVS-ADF) model by extending the ADF model in state-space form. The TVS-ADF model considers time-varying coefficients and a time-varying variance–covariance matrix of its innovation terms, so that it can capture complex dynamic economic characteris- tics. We propose the identification scheme of the common factors in the TVS-ADF and derive the identification theory. We also propose an effective Markov chain Monte Carlo (MCMC) algorithm to estimate the TVS-ADF. To avoid the overparameterization caused by the time-varying characteristics of the TVS-ADF, we include the shrinkage and sparsification approaches in the MCMC algorithm. Additionally, we propose several effective information criteria for the determination of the number of factors in the TVS-ADF. Extensive artificial simulations demonstrate that the TVS-ADF has better forecast performance than the ADF in almost all settings for different numbers of explained variables, numbers of explanatory variables, sparsity levels, and sample sizes. An empirical application to macroeconomic forecasting also indicates that our model can substantially improve predictive accuracy and capture the dynamic features of an economic system better than the ADF. |
| Keywords: | MCMC, shrinkage, sparsification, overparameterization, algorithms |
| Date: | 2024–01 |
| URL: | https://d.repec.org/n?u=RePEc:nan:wpaper:2401 |
| By: | Miguel Alves Pereira |
| Abstract: | This article proposes predictive economics as a distinct analytical perspective within economics, grounded in machine learning and centred on predictive accuracy rather than causal identification. Drawing on the instrumentalist tradition (Friedman), the explanation-prediction divide (Shmueli), and the contrast between modelling cultures (Breiman), we formalise prediction as a valid epistemological and methodological objective. Reviewing recent applications across economic subfields, we show how predictive models contribute to empirical analysis, particularly in complex or data-rich contexts. This perspective complements existing approaches and supports a more pluralistic methodology - one that values out-of-sample performance alongside interpretability and theoretical structure. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.04726 |
| By: | Leonardo N. Ferreira; Haroon Mumtaz; Ana Skoblar |
| Abstract: | This paper introduces a Bayesian vector autoregression (BVAR) with stochastic volatility-in-mean and time-varying skewness. Unlike previous approaches, the proposed model allows both volatility and skewness to directly affect macroeconomic variables. We provide a Gibbs sampling algorithm for posterior inference and apply the model to quarterly data for the US and the UK. Empirical results show that skewness shocks have economically significant effects on output, inflation and spreads, often exceeding the impact of volatility shocks. In a pseudo-real-time forecasting exercise, the proposed model outperforms existing alternatives in many cases. Moreover, the model produces sharper measures of tail risk, revealing that standard stochastic volatility models tend to overstate uncertainty. These findings highlight the importance of incorporating time-varying skewness for capturing macro-financial risks and improving forecast performance. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.08415 |
| By: | Samuel N. Cohen; Cephas Svosve |
| Abstract: | We explore a link between stochastic volatility (SV) and path-dependent volatility (PDV) models. Using assumed density filtering, we map a given SV model into a corresponding PDV representation. The resulting specification is lightweight, improves in-sample fit, and delivers robust out-of-sample forecasts. We also introduce a calibration procedure for both SV and PDV models that produces standard errors for parameter estimates and supports joint calibration of SPX/VIX smile. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.02024 |
| By: | Kairan Hong; Jinling Gan; Qiushi Tian; Yanglinxuan Guo; Rui Guo; Runnan Li |
| Abstract: | Cryptocurrency markets present unique prediction challenges due to their extreme volatility, 24/7 operation, and hypersensitivity to news events, with existing approaches suffering from key information extraction and poor sideways market detection critical for risk management. We introduce a theoretically-grounded multi-agent cryptocurrency trend prediction framework that advances the state-of-the-art through three key innovations: (1) an information-preserving news analysis system with formal theoretical guarantees that systematically quantifies market impact, regulatory implications, volume dynamics, risk assessment, technical correlation, and temporal effects using large language models; (2) an adaptive volatility-conditional fusion mechanism with proven optimal properties that dynamically combines news sentiment and technical indicators based on market regime detection; (3) a distributed multi-agent coordination architecture with low communication complexity enabling real-time processing of heterogeneous data streams. Comprehensive experimental evaluation on Bitcoin across three prediction horizons demonstrates statistically significant improvements over state-of-the-art natural language processing baseline, establishing a new paradigm for financial machine learning with broad implications for quantitative trading and risk management systems. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.08268 |
| By: | Andrew Foerster; Andreas Hornstein; Pierre-Daniel Sarte; Mark W. Watson |
| Abstract: | We explore the evolving significance of different production sectors within the U.S. economy since World War II and provide methods for estimating and forecasting these shifts. Using a compositional accounting approach, we find that the well-documented transition from goods to services is primarily driven by two compositional changes: 1) the rise of Intellectual Property Products (IPP) as an input producer, replacing Durable Goods almost one-for-one in terms of input shares in virtually all sectors; and 2) a shift in consumer spending from Nondurable Goods to Services. A structural model replicating these shifts reveals that the rise of IPP at the expense of Durable Goods is largely explained by increases in the efficiency of IPP inputs used in production: input-biased technical change. Trend variations in sectoral total factor productivity, and their attendant effects on relative prices and income, are the main driver of evolving consumption patterns. Both reduced-form and structural forecasts project these trends to continue over the next two decades, albeit at lower rates, indicating a slower pace of structural change. |
| JEL: | E17 E23 E27 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34338 |
| By: | Byeungchun Kwon; Taejin Park; Phurichai Rungcharoenkitkul; Frank Smets |
| Abstract: | Macroeconomic indicators provide quantitative signals that must be pieced together and interpreted by economists. We propose a reversed approach of parsing press narratives directly using Large Language Models (LLM) to recover growth and inflation sentiment indices. A key advantage of this LLM-based approach is the ability to decompose aggregate sentiment into its drivers, readily enabling an interpretation of macroeconomic dynamics. Our sentiment indices track hard-data counterparts closely, providing an accurate, near real-time picture of the macroeconomy. Their components–demand, supply, and deeper structural forces–are intuitive and consistent with prior model-based studies. Incorporating sentiment indices improves the forecasting performance of simple statistical models, pointing to information unspanned by traditional data. |
| Keywords: | macroeconomic sentiment, growth, inflation, monetary policy, fiscal policy, LLMs, machine learning |
| JEL: | E30 E44 E60 C55 C82 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:bis:biswps:1294 |
| By: | Yanran Wu; Xinlei Zhang; Quanyi Xu; Qianxin Yang; Chao Zhang |
| Abstract: | We build a 167-indicator comprehensive credit risk indicator set, integrating macro, corporate financial, bond-specific indicators, and for the first time, 30 large-scale corporate non-financial indicators. We use seven machine learning models to construct a bond credit spread prediction model, test their spread predictive power and economic mechanisms, and verify their credit rating prediction effectiveness. Results show these models outperform Chinese credit rating agencies in explaining credit spreads. Specially, adding non-financial indicators more than doubles their out-of-sample performance vs. traditional feature-driven models. Mechanism analysis finds non-financial indicators far more important than traditional ones (macro-level, financial, bond features)-seven of the top 10 are non-financial (e.g., corporate governance, property rights nature, information disclosure evaluation), the most stable predictors. Models identify high-risk traits (deteriorating operations, short-term debt, higher financing constraints) via these indicators for spread prediction and risk identification. Finally, we pioneer a credit rating model using predicted spreads (predicted implied rating model), with full/sub-industry models achieving over 75% accuracy, recall, F1. This paper provides valuable guidance for bond default early warning, credit rating, and financial stability. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.19042 |
| By: | Masahiro Kato; Kentaro Baba; Hibiki Kaibuchi; Ryo Inokuchi |
| Abstract: | Portfolio optimization is a critical task in investment. Most existing portfolio optimization methods require information on the distribution of returns of the assets that make up the portfolio. However, such distribution information is usually unknown to investors. Various methods have been proposed to estimate distribution information, but their accuracy greatly depends on the uncertainty of the financial markets. Due to this uncertainty, a model that could well predict the distribution information at one point in time may perform less accurately compared to another model at a different time. To solve this problem, we investigate a method for portfolio optimization based on Bayesian predictive synthesis (BPS), one of the Bayesian ensemble methods for meta-learning. We assume that investors have access to multiple asset return prediction models. By using BPS with dynamic linear models to combine these predictions, we can obtain a Bayesian predictive posterior about the mean rewards of assets that accommodate the uncertainty of the financial markets. In this study, we examine how to construct mean-variance portfolios and quantile-based portfolios based on the predicted distribution information. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.07180 |
| By: | Yoontae Hwang; Stefan Zohren |
| Abstract: | Robust asset allocation is a key challenge in quantitative finance, where deep-learning forecasters often fail due to objective mismatch and error amplification. We introduce the Signature-Informed Transformer (SIT), a novel framework that learns end-to-end allocation policies by directly optimizing a risk-aware financial objective. SIT's core innovations include path signatures for a rich geometric representation of asset dynamics and a signature-augmented attention mechanism embedding financial inductive biases, like lead-lag effects, into the model. Evaluated on daily S\&P 100 equity data, SIT decisively outperforms traditional and deep-learning baselines, especially when compared to predict-then-optimize models. These results indicate that portfolio-aware objectives and geometry-aware inductive biases are essential for risk-aware capital allocation in machine-learning systems. The code is available at: https://github.com/Yoontae6719/Signature -Informed-Transformer-For-Asset-Allocati on |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.03129 |
| By: | Okan Akarsu; Huzeyfe Torun |
| Abstract: | [EN] This study investigates how firms' inflation expectations evolve during both inflationary and disinflationary periods, using data from the Business Tendency Survey (BTS), and analyzes the relationship between firms' cost- and price-related expectations and their inflation forecasts. By matching firms based on their fundamental characteristics, and then employing probit models alongside propensity score matching, we estimate the average treatment effect on the treated (ATT) to assess changes in expectations as quasi-randomized treatments. Our results indicate a significant dispersion in inflation expectations during periods of rising inflation. However, as inflation stabilizes, firms' expectations begin to converge, signaling reduced uncertainty and closer alignment with central bank targets. This study adds to the literature by providing empirical evidence from an emerging market economy, offering valuable insights into how firm-level inflation expectations shift across different inflation phases and highlighting the role of monetary policy in anchoring these expectations. [TR] Bu calisma, Iktisadi Egilim Anketi (IYA) verilerini kullanarak firmalarin enflasyon beklentilerinin hem enflasyonist hem de dezenflasyonist donemlerde nasil degistigini Iktisadi Egilim Anketi (IYA) verilerini kullanarak incelenmekte ve firmalarin maliyet ve fiyatla ilgili beklentileri ile enflasyon tahminleri arasindaki iliskiyi analiz etmektedir. Firmalari temel ozelliklerine gore eslestirip, ardindan egilim puani eslestirme) yontemi ve probit modeller kullanilarak, beklentilerdeki degisiklikleri yari-rastgele tedaviler olarak degerlendirmek icin tedavi grubu uzerindeki ortalama etki tahmin edilmektedir. Bulgularimiz, enflasyonun yukseldigi donemlerde firmalarin enflasyon beklentilerinde onemli bir dagilim oldugunu gostermektedir. Ancak, enflasyon istikrar kazandikca, firmalarin beklentileri yakinsamaya baslamakta; bu da belirsizligin azaldigini ve beklentilerin merkez bankasi hedeflerine daha yakin hale geldigine isaret etmektedir. Bu calisma, gelismekte olan bir ulkeden elde edilen ampirik bulgularla mevcut literature katki saglamakta; firmalarin enflasyon beklentilerinin enflasyonun farkli evrelerinde nasil degistigine dair degerli bilgiler sunmakta ve bu beklentilerin cipalanmasinda para politikasinin rolunu vurgulamaktadir. |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:tcb:econot:2519 |
| By: | Okan Akarsu; Altan Aldan; Unal Seven |
| Abstract: | [EN] This study examines how firms’ perceptions of their business environment and expectations of their own future sales shape their inflation expectations, with particular attention to differences across inflation regimes. Using firm-level data from Türkiye’s Business Tendency Survey (BTS), we find that firms with a weaker assessment of their industry’s current state tend to forecast higher inflation, particularly during periods of heightened uncertainty. In high-inflation environments, firms’ inflation expectations are more strongly associated with their perceptions of current industry conditions, whereas expectations about their own future sales play a comparatively smaller role. Conversely, firms' expectations for their own sales become a more decisive factor in their inflation forecasts during low-inflation or disinflationary periods. These results highlight the state-dependent nature of firms’ inflation expectation formation. By shedding light on the mechanisms through which firms form expectations, this note underscores how reducing macroeconomic uncertainty and strengthening policy communication can enhance the effectiveness of monetary policy. [TR] Bu calisma, firmalarin sektorel duruma iliskin algilarinin ve satis beklentilerinin enflasyon beklentilerini nasil sekillendirdigini, farkli enflasyon rejimleri altinda incelemektedir. Iktisadi Yonelim Anketi (IYA) verilerini kullanan analizimiz, sektorlerinin mevcut durumuna iliskin gorece daha olumsuz beklentiler icinde olan firmalarin, ozellikle belirsizligin arttigi donemlerde, diger firmalara gore daha yuksek enflasyon tahmininde bulunma egiliminde olduklarini gostermektedir. Yuksek enflasyon ortaminda, firmalarin enflasyon beklentileri, sektorlerinin mevcut kosullarina iliskin algilariyla daha guclu bir sekilde iliskilidir; buna karsilik, gelecekteki satislarina iliskin beklentilerinin enflasyon bekleyisleri uzerine etkisi daha sinirli kalmaktadir. Buna karsin, dusuk enflasyon veya dezenflasyon donemlerinde, firmalarin satis beklentileri enflasyon tahminlerinde daha belirleyici bir faktor haline gelmektedir. Bu bulgular, firmalarin enflasyon beklenti olusumunun duruma bagli bir yapiya sahip oldugunu ortaya koymaktadir. Firmalarin beklenti olusturma mekanizmalarina isik tutan bu calisma, makroekonomik belirsizligin azaltilmasi ve politika iletisiminin guclendirilmesinin para politikasinin etkinligini artirmada onemli bir rol oynayabilecegini vurgulamaktadir. |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:tcb:econot:2521 |
| By: | Kremens, Lukas; Martin, Ian; Varela, Liliana |
| Abstract: | We study exchange rate expectations in surveys of financial professionals and find that they successfully forecast currency appreciation at the two-year horizon, both in and out of sample. Exchange rate expectations are also interpretable, in the sense that three macrofinance variables—the risk-neutral covariance between the exchange rate and equity market, the real exchange rate, and the current account relative to GDP—explain most of their variation. But there is no “secret sauce” in expectations: after controlling for the three macro-finance variables, the residual information in survey expectations does not forecast currency appreciation in our sample. |
| JEL: | F3 G3 |
| Date: | 2025–09–29 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:127790 |
| By: | Okan Akarsu; Emrehan Aktug; Altan Aldan; Unal Seven |
| Abstract: | This paper leverages a rare quasi-experiment—the unexpected September 2021 interest rate cut by the Central Bank of the Republic of Türkiye—to examine how inflation expectations shape firm behavior in a high-inflation environment. Drawing on a rich dataset that combines monthly survey responses with administrative records, we exploit the heterogeneous revisions in firms’ inflation expectations triggered by the policy shock. Firms that significantly increased their inflation forecasts (treated) subsequently became more pessimistic about economic conditions, reduced employment, and curbed domestic sales. At the same time, they strategically raised procurement, acquired more foreign currency assets, and boosted borrowing in local currency—even at higher costs—in anticipation of debt erosion. These patterns suggest that firms’ heightened inflation expectations drive both defensive and opportunistic behaviors, ranging from hedging against currency depreciation to locking in lower financing costs. Overall, the findings highlight the critical role of inflation expectations in guiding firm-level decisions and document the importance of policy credibility in volatile macroeconomic settings. |
| Keywords: | Inflation expectations, Firm behaviors, High inflation, Experimental macroeconomics |
| JEL: | E12 E24 E31 E52 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:tcb:wpaper:2516 |
| By: | Anna Cole; Michael W. McCracken |
| Abstract: | What anchors inflation expectations? An analysis breaks down how anticipated drift from the Fed’s 2% target and divergence among forecasts each contribute. |
| Keywords: | inflation; inflation expectations; inflation forecasts; inflation expectations anchoring; Survey of Professional Forecasters (SPF); Michigan Survey of Consumers |
| Date: | 2025–09–25 |
| URL: | https://d.repec.org/n?u=RePEc:fip:l00001:101823 |