nep-cmp New Economics Papers
on Computational Economics
Issue of 2023‒05‒29
twenty-two papers chosen by
Stan Miles
Thompson Rivers University

  1. Learning Volatility Surfaces using Generative Adversarial Networks By Andrew Na; Meixin Zhang; Justin Wan
  2. Random neural networks for rough volatility By Antoine Jacquier; Zan Zuric
  3. UQ for Credit Risk Management: A deep evidence regression approach By Ashish Dhiman
  4. Conditional Generative Models for Learning Stochastic Processes By Salvatore Certo; Anh Pham; Nicolas Robles; Andrew Vlasic
  5. Deep learning techniques for financial time series forecasting: A review of recent advancements: 2020-2022 By Cheng Zhang; Nilam Nur Amir Sjarif; Roslina Binti Ibrahim
  6. TM-vector: A Novel Forecasting Approach for Market stock movement with a Rich Representation of Twitter and Market data By Faraz Sasani; Ramin Mousa; Ali Karkehabadi; Samin Dehbashi; Ali Mohammadi
  7. Stock Price Predictability and the Business Cycle via Machine Learning By Li Rong Wang; Hsuan Fu; Xiuyi Fan
  8. Hedonic Prices and Quality Adjusted Price Indices Powered by AI By Patrick Bajari; Zhihao Cen; Victor Chernozhukov; Manoj Manukonda; Suhas Vijaykumar; Jin Wang; Ramon Huerta; Junbo Li; Ling Leng; George Monokroussos; Shan Wan
  9. LSTM based Anomaly Detection in Time Series for United States exports and imports By Aggarwal, Sakshi
  10. Optimum Output Long Short-Term Memory Cell for High-Frequency Trading Forecasting By Adamantios Ntakaris; Moncef Gabbouj; Juho Kanniainen
  11. Identifying Financial Crises Using Machine Learning on Textual Data By Mary Chen; Matthew DeHaven; Isabel Kitschelt; Seung Jung Lee; Martin Sicilian
  12. The impact of the AI revolution on asset management By Michael Kopp
  13. Maximally Machine-Learnable Portfolios By Philippe Goulet Coulombe; Maximilian Gobel
  14. “Making Text Talk”: The Minutes of the Central Bank of Brazil and the Real Economy By Carlos Moreno Pérez; Marco Minozzo
  15. Estimating Input Coefficients for Regional Input-Output Tables Using Deep Learning with Mixup By Shogo Fukui
  16. Augmented balancing weights as linear regression By David Bruns-Smith; Oliver Dukes; Avi Feller; Elizabeth L. Ogburn
  17. Deep Stock: training and trading scheme using deep learning By Sungwoo Kang
  18. Financial Hedging and Risk Compression, A journey from linear regression to neural network By Ali Shirazi; Fereshteh Sadeghi Naieni Fard
  19. Geoeconomics, Structural Change and Energy Use in Iran: A SAM-Based CGE Analysis with Some Geoeconomic and Geopolitical Considerations By Khan, Haider
  20. Double and Single Descent in Causal Inference with an Application to High-Dimensional Synthetic Control By Jann Spiess; Guido Imbens; Amar Venugopal
  21. Identifying Trades Using Technical Analysis and ML/DL Models By Aayush Shah; Mann Doshi; Meet Parekh; Nirmit Deliwala; Prof. Pramila M. Chawan
  22. Assessing Text Mining and Technical Analyses on Forecasting Financial Time Series By Ali Lashgari

  1. By: Andrew Na; Meixin Zhang; Justin Wan
    Abstract: In this paper, we propose a generative adversarial network (GAN) approach for efficiently computing volatility surfaces. The idea is to make use of the special GAN neural architecture so that on one hand, we can learn volatility surfaces from training data and on the other hand, enforce no-arbitrage conditions. In particular, the generator network is assisted in training by a discriminator that evaluates whether the generated volatility matches the target distribution. Meanwhile, our framework trains the GAN network to satisfy the no-arbitrage constraints by introducing penalties as regularization terms. The proposed GAN model allows the use of shallow networks which results in much less computational costs. In our experiments, we demonstrate the performance of the proposed method by comparing with the state-of-the-art methods for computing implied and local volatility surfaces. We show that our GAN model can outperform artificial neural network (ANN) approaches in terms of accuracy and computational time.
    Date: 2023–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2304.13128&r=cmp
  2. By: Antoine Jacquier; Zan Zuric
    Abstract: We construct a deep learning-based numerical algorithm to solve path-dependent partial differential equations arising in the context of rough volatility. Our approach is based on interpreting the PDE as a solution to an SPDE, building upon recent insights by Bayer, Qiu and Yao, and on constructing a neural network of reservoir type as originally developed by Gonon, Grigoryeva, Ortega. The reservoir approach allows us to formulate the optimisation problem as a simple least-square regression for which we prove theoretical convergence properties.
    Date: 2023–05
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2305.01035&r=cmp
  3. By: Ashish Dhiman
    Abstract: Machine Learning has invariantly found its way into various Credit Risk applications. Due to the intrinsic nature of Credit Risk, quantifying the uncertainty of the predicted risk metrics is essential, and applying uncertainty-aware deep learning models to credit risk settings can be very helpful. In this work, we have explored the application of a scalable UQ-aware deep learning technique, Deep Evidence Regression and applied it to predicting Loss Given Default. We contribute to the literature by extending the Deep Evidence Regression methodology to learning target variables generated by a Weibull process and provide the relevant learning framework. We demonstrate the application of our approach to both simulated and real-world data.
    Date: 2023–05
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2305.04967&r=cmp
  4. By: Salvatore Certo; Anh Pham; Nicolas Robles; Andrew Vlasic
    Abstract: A framework to learn a multi-modal distribution is proposed, denoted as the Conditional Quantum Generative Adversarial Network (C-qGAN). The neural network structure is strictly within a quantum circuit and, as a consequence, is shown to represents a more efficient state preparation procedure than current methods. This methodology has the potential to speed-up algorithms, such as Monte Carlo analysis. In particular, after demonstrating the effectiveness of the network in the learning task, the technique is applied to price Asian option derivatives, providing the foundation for further research on other path-dependent options.
    Date: 2023–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2304.10382&r=cmp
  5. By: Cheng Zhang; Nilam Nur Amir Sjarif; Roslina Binti Ibrahim
    Abstract: Forecasting financial time series has long been a challenging problem that has attracted attention from both researchers and practitioners. Statistical and machine learning techniques have both been explored to develop effective forecasting models in the past few decades. With recent developments in deep learning models, financial time series forecasting models have advanced significantly, and these developments are often difficult to keep up with. Hence, we have conducted this literature review to provide a comprehensive assessment of recent research from 2020 to 2022 on deep learning models used to predict prices based on financial time series. Our review presents different data sources and neural network structures, as well as their implementation details. Our goals are to ensure that interested researchers remain up-to-date on recent developments in the field and facilitate the selection of baselines based on models used in prior studies. Additionally, we provide suggestions for future research based on the content in this review.
    Date: 2023–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2305.04811&r=cmp
  6. By: Faraz Sasani; Ramin Mousa; Ali Karkehabadi; Samin Dehbashi; Ali Mohammadi
    Abstract: Stock market forecasting has been a challenging part for many analysts and researchers. Trend analysis, statistical techniques, and movement indicators have traditionally been used to predict stock price movements, but text extraction has emerged as a promising method in recent years. The use of neural networks, especially recurrent neural networks, is abundant in the literature. In most studies, the impact of different users was considered equal or ignored, whereas users can have other effects. In the current study, we will introduce TM-vector and then use this vector to train an IndRNN and ultimately model the market users' behaviour. In the proposed model, TM-vector is simultaneously trained with both the extracted Twitter features and market information. Various factors have been used for the effectiveness of the proposed forecasting approach, including the characteristics of each individual user, their impact on each other, and their impact on the market, to predict market direction more accurately. Dow Jones 30 index has been used in current work. The accuracy obtained for predicting daily stock changes of Apple is based on various models, closed to over 95\% and for the other stocks is significant. Our results indicate the effectiveness of TM-vector in predicting stock market direction.
    Date: 2023–03
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2304.02094&r=cmp
  7. By: Li Rong Wang; Hsuan Fu; Xiuyi Fan
    Abstract: We study the impacts of business cycles on machine learning (ML) predictions. Using the S&P 500 index, we find that ML models perform worse during most recessions, and the inclusion of recession history or the risk-free rate does not necessarily improve their performance. Investigating recessions where models perform well, we find that they exhibit lower market volatility than other recessions. This implies that the improved performance is not due to the merit of ML methods but rather factors such as effective monetary policies that stabilized the market. We recommend that ML practitioners evaluate their models during both recessions and expansions.
    Date: 2023–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2304.09937&r=cmp
  8. By: Patrick Bajari; Zhihao Cen; Victor Chernozhukov; Manoj Manukonda; Suhas Vijaykumar; Jin Wang; Ramon Huerta; Junbo Li; Ling Leng; George Monokroussos; Shan Wan
    Abstract: Accurate, real-time measurements of price index changes using electronic records are essential for tracking inflation and productivity in today's economic environment. We develop empirical hedonic models that can process large amounts of unstructured product data (text, images, prices, quantities) and output accurate hedonic price estimates and derived indices. To accomplish this, we generate abstract product attributes, or ``features, '' from text descriptions and images using deep neural networks, and then use these attributes to estimate the hedonic price function. Specifically, we convert textual information about the product to numeric features using large language models based on transformers, trained or fine-tuned using product descriptions, and convert the product image to numeric features using a residual network model. To produce the estimated hedonic price function, we again use a multi-task neural network trained to predict a product's price in all time periods simultaneously. To demonstrate the performance of this approach, we apply the models to Amazon's data for first-party apparel sales and estimate hedonic prices. The resulting models have high predictive accuracy, with $R^2$ ranging from $80\%$ to $90\%$. Finally, we construct the AI-based hedonic Fisher price index, chained at the year-over-year frequency. We contrast the index with the CPI and other electronic indices.
    Date: 2023–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2305.00044&r=cmp
  9. By: Aggarwal, Sakshi
    Abstract: This survey aims to offer a thorough and organized overview of research on anomaly detection, which is a significant problem that has been studied in various fields and application areas. Some anomaly detection techniques have been tailored for specific domains, while others are more general. Anomaly detection involves identifying unusual patterns or events in a dataset, which is important for a wide range of applications including fraud detection and medical diagnosis. Not much research on anomaly detection techniques has been conducted in the field of economic and international trade. Therefore, this study attempts to analyze the time-series data of United Nations exports and imports for the period 1992 – 2022 using LSTM based anomaly detection algorithm. Deep learning, particularly LSTM networks, are becoming increasingly popular in anomaly detection tasks due to their ability to learn complex patterns in sequential data. This paper presents a detailed explanation of LSTM architecture, including the role of input, forget, and output gates in processing input vectors and hidden states at each timestep. The LSTM based anomaly detection approach yields promising results by modelling small-term as well as long-term temporal dependencies.
    Keywords: Anomaly detection, LSTM, Machine learning, Artificial intelligence, economic trade
    JEL: C54 F13 F15
    Date: 2023–04–25
    URL: http://d.repec.org/n?u=RePEc:pra:mprapa:117149&r=cmp
  10. By: Adamantios Ntakaris; Moncef Gabbouj; Juho Kanniainen
    Abstract: High-frequency trading requires fast data processing without information lags for precise stock price forecasting. This high-paced stock price forecasting is usually based on vectors that need to be treated as sequential and time-independent signals due to the time irregularities that are inherent in high-frequency trading. A well-documented and tested method that considers these time-irregularities is a type of recurrent neural network, named long short-term memory neural network. This type of neural network is formed based on cells that perform sequential and stale calculations via gates and states without knowing whether their order, within the cell, is optimal. In this paper, we propose a revised and real-time adjusted long short-term memory cell that selects the best gate or state as its final output. Our cell is running under a shallow topology, has a minimal look-back period, and is trained online. This revised cell achieves lower forecasting error compared to other recurrent neural networks for online high-frequency trading forecasting tasks such as the limit order book mid-price prediction as it has been tested on two high-liquid US and two less-liquid Nordic stocks.
    Date: 2023–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2304.09840&r=cmp
  11. By: Mary Chen; Matthew DeHaven; Isabel Kitschelt; Seung Jung Lee; Martin Sicilian
    Abstract: We use machine learning techniques on textual data to identify financial crises. The onset of a crisis and its duration have implications for real economic activity, and as such can be valuable inputs into macroprudential, monetary, and fiscal policy. The academic literature and the policy realm rely mostly on expert judgment to determine crises, often with a lag. Consequently, crisis durations and the buildup phases of vulnerabilities are usually determined only with the benefit of hindsight. Although we can identify and forecast a portion of crises worldwide to various degrees with traditional econometric techniques and using readily available market data, we find that textual data helps in reducing false positives and false negatives in out-of-sample testing of such models, especially when the crises are considered more severe. Building a framework that is consistent across countries and in real time can benefit policymakers around the world, especially when international coordination is required across different government policies.
    Keywords: Financial crises; Machine learning; Natural language processing
    JEL: C53 C55 G01
    Date: 2023–03–31
    URL: http://d.repec.org/n?u=RePEc:fip:fedgif:1374&r=cmp
  12. By: Michael Kopp
    Abstract: Recent progress in deep learning, a special form of machine learning, has led to remarkable capabilities machines can now be endowed with: they can read and understand free flowing text, reason and bargain with human counterparts, translate texts between languages, learn how to take decisions to maximize certain outcomes, etc. Today, machines have revolutionized the detection of cancer, the prediction of protein structures, the design of drugs, the control of nuclear fusion reactors etc. Although these capabilities are still in their infancy, it seems clear that their continued refinement and application will result in a technological impact on nearly all social and economic areas of human activity, the likes of which we have not seen before. In this article, I will share my view as to how AI will likely impact asset management in general and I will provide a mental framework that will equip readers with a simple criterion to assess whether and to what degree a given fund really exploits deep learning and whether a large disruption risk from deep learning exist.
    Date: 2023–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2304.10212&r=cmp
  13. By: Philippe Goulet Coulombe (University of Quebec in Montreal); Maximilian Gobel (Bocconi University)
    Abstract: When it comes to stock returns, any form of predictability can bolster risk-adjusted profitability. We develop a collaborative machine learning algorithm that optimizes portfolio weights so that the resulting synthetic security is maximally predictable. Precisely, we introduce MACE, a multivariate extension of Alternating Conditional Expectations that achieves the aforementioned goal by wielding a Random Forest on one side of the equation, and a constrained Ridge Regression on the other. There are two key improvements with respect to Lo and MacKinlay’s original maximally predictable portfolio approach. First, it accommodates for any (nonlinear) forecasting algorithm and predictor set. Second, it handles large portfolios. We conduct exercises at the daily and monthly frequency and report significant increases in predictability and profitability using very little conditioning information. Interestingly, predictability is found in bad as well as good times, and MACE successfully navigates the debacle of 2022.
    Date: 2023–04
    URL: http://d.repec.org/n?u=RePEc:bbh:wpaper:23-01&r=cmp
  14. By: Carlos Moreno Pérez (Banco de España); Marco Minozzo (University of Verona)
    Abstract: This paper investigates the relationship between the views expressed in the minutes of the meetings of the Central Bank of Brazil’s Monetary Policy Committee (COPOM) and the real economy. It applies various computational linguistic machine learning algorithms to construct measures of the minutes of the COPOM. First, we create measures of the content of the paragraphs of the minutes using Latent Dirichlet Allocation (LDA). Second, we build an uncertainty index for the minutes using Word Embedding and K-Means. Then, we combine these indices to create two topic-uncertainty indices. The first one is constructed from paragraphs with a higher probability of topics related to “general economic conditions”. The second topic-uncertainty index is constructed from paragraphs that have a higher probability of topics related to “inflation” and the “monetary policy discussion”. Finally, we employ a structural VAR model to explore the lasting effects of these uncertainty indices on certain Brazilian macroeconomic variables. Our results show that greater uncertainty leads to a decline in inflation, the exchange rate, industrial production and retail trade in the period from January 2000 to July 2019.
    Keywords: Central Bank of Brazil, monetary policy communication, Latent Dirichlet Allocation, monetary policy uncertainty, Structural Vector Autoregressive model, Word Embedding
    JEL: C32 C45 D83 E52
    Date: 2022–11
    URL: http://d.repec.org/n?u=RePEc:bde:wpaper:2240&r=cmp
  15. By: Shogo Fukui
    Abstract: An input-output table is an important data for analyzing the economic situation of a region. Generally, the input-output table for each region (regional input-output table) in Japan is not always publicly available, so it is necessary to estimate the table. In particular, various methods have been developed for estimating input coefficients, which are an important part of the input-output table. Currently, non-survey methods are often used to estimate input coefficients because they require less data and computation, but these methods have some problems, such as discarding information and requiring additional data for estimation. In this study, the input coefficients are estimated by approximating the generation process with an artificial neural network (ANN) to mitigate the problems of the non-survey methods and to estimate the input coefficients with higher precision. To avoid over-fitting due to the small data used, data augmentation, called mixup, is introduced to increase the data size by generating virtual regions through region composition and scaling. By comparing the estimates of the input coefficients with those of Japan as a whole, it is shown that the accuracy of the method of this research is higher and more stable than that of the conventional non-survey methods. In addition, the estimated input coefficients for the three cities in Japan are generally close to the published values for each city.
    Date: 2023–05
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2305.01201&r=cmp
  16. By: David Bruns-Smith; Oliver Dukes; Avi Feller; Elizabeth L. Ogburn
    Abstract: We provide a novel characterization of augmented balancing weights, also known as Automatic Debiased Machine Learning (AutoDML). These estimators combine outcome modeling with balancing weights, which estimate inverse propensity score weights directly. When the outcome and weighting models are both linear in some (possibly infinite) basis, we show that the augmented estimator is equivalent to a single linear model with coefficients that combine the original outcome model coefficients and OLS; in many settings, the augmented estimator collapses to OLS alone. We then extend these results to specific choices of outcome and weighting models. We first show that the combined estimator that uses (kernel) ridge regression for both outcome and weighting models is equivalent to a single, undersmoothed (kernel) ridge regression; this also holds when considering asymptotic rates. When the weighting model is instead lasso regression, we give closed-form expressions for special cases and demonstrate a ``double selection'' property. Finally, we generalize these results to linear estimands via the Riesz representer. Our framework ``opens the black box'' on these increasingly popular estimators and provides important insights into estimation choices for augmented balancing weights.
    Date: 2023–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2304.14545&r=cmp
  17. By: Sungwoo Kang
    Abstract: Despite the efficient market hypothesis, many studies suggest the existence of inefficiencies in the stock market, leading to the development of techniques to gain above-market returns, known as alpha. Systematic trading has undergone significant advances in recent decades, with deep learning emerging as a powerful tool for analyzing and predicting market behavior. In this paper, we propose a model inspired by professional traders that look at stock prices of the previous 600 days and predicts whether the stock price rises or falls by a certain percentage within the next D days. Our model, called DeepStock, uses Resnet's skip connections and logits to increase the probability of a model in a trading scheme. We test our model on both the Korean and US stock markets and achieve a profit of N\% on Korea market, which is M\% above the market return, and profit of A\% on US market, which is B\% above the market return.
    Date: 2023–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2304.14870&r=cmp
  18. By: Ali Shirazi; Fereshteh Sadeghi Naieni Fard
    Abstract: Finding the hedge ratios for a portfolio and risk compression is the same mathematical problem. Traditionally, regression is used for this purpose. However, regression has its own limitations. For example, in a regression model, we can't use highly correlated independent variables due to multicollinearity issue and instability in the results. A regression model cannot also consider the cost of hedging in the hedge ratios estimation. We have introduced several methods that address the linear regression limitation while achieving better performance. These models, in general, fall into two categories: Regularization Techniques and Common Factor Analyses. In regularization techniques, we minimize the variance of hedged portfolio profit and loss (PnL) and the hedge ratio sizes, which helps reduce the cost of hedging. The regularization techniques methods could also consider the cost of hedging as a function of the cost of funding, market condition, and liquidity. In common factor analyses, we first map variables into common factors and then find the hedge ratios so that the hedged portfolio doesn't have any exposure to the factors. We can use linear or nonlinear factors construction. We are introducing a modified beta variational autoencoder that constructs common factors nonlinearly to compute hedges. Finally, we introduce a comparison method and generate numerical results for an example.
    Date: 2023–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2305.04801&r=cmp
  19. By: Khan, Haider
    Abstract: In this paper we present a structural CGE model for analyzing the energy situation in Iran and to draw some tentative economic policy and geopolitical conclusions. An important feature of the Iranian economy is its constant intensification of energy use per unit of labor. At the same time, Iran shows only slow improvement in energy intensity i.e. the use of energy per unit of output. Our structural computable general equilibrium (CGE) model for Iran is based on 3- aggregate productive activities input-output structure- agriculture, energy and industry ---within a social accounting matrix for Iran. Four simulation exercises are conducted using this model--- industrial investment demand increase, industrial wage increase, exchange rate depreciation, and government spending increase in industry. Our results show that structural change associated with raising industrial labor productivity and employment share are likely to result in simultaneous intensification of per worker energy-use and slight reduction of energy productivity in Iran. Industrial wage increase can create cost-push inflation and output contraction through a decrease in input use and increase in imports. Exchange rate devaluation is expansionary. Furthermore, when industrial output is insulated from foreign-domestic relative price effects, devaluation too becomes contractionary and wage increase results in a slight contraction in real GDP due to the "forced saving" effect. The model illustrates some of policy challenges Iran faces in its attempt to achieve "green growth" objective with high level of employment. To implement socially beneficial, capabilities- enhancing wage-led growth, Iran has to first successfully rebalance from its export-oriented growth path, which might require the government providing better social safety net for its citizens and increase their purchasing power across the board and generate further productive capacity in the Agricultural sector rather than generate inflation by increasing just the industrial sector wage. This would require a careful crafting of guaranteed income esp. for the Agricultural sector and government programs and incentives for increasing supply and productivity by enhancing both physical infrastructure, technical change and human capabilities. Geopolitically, Iran’s current competition with Saudi Arabia and Turkey diverts valuable economic resources from development to political purposes. Satisfying legitimate security concerns rationally while reorienting the geopolitical concerns to a peaceful commercial relation to North and East of Iran including Japan will lead to much more stable and prosperous economic conditions than Iran experiences at present. However, provocations such as the June 2017 Qatar crisis provoked by Saudi Arabia and its “Islamic NATO” alliance makes geopolitical complexities more acute for Iran. Still Iran needs to avoid sanguinary conflicts and try to isolate Saudi Arabia politically. Geopolitical, 2023 moves for reconciliation via China and Russia seem to indicate a northward and eastward direction of energy and other related policies of both Iran and Saudi Arabia.
    Keywords: Energy , Geoeconomics, geopolitics, Iran, Saudi Arabia, Russia, China, CGE modeling
    JEL: F4 F51 Q4
    Date: 2023–04–15
    URL: http://d.repec.org/n?u=RePEc:pra:mprapa:117155&r=cmp
  20. By: Jann Spiess; Guido Imbens; Amar Venugopal
    Abstract: Motivated by a recent literature on the double-descent phenomenon in machine learning, we consider highly over-parametrized models in causal inference, including synthetic control with many control units. In such models, there may be so many free parameters that the model fits the training data perfectly. As a motivating example, we first investigate high-dimensional linear regression for imputing wage data, where we find that models with many more covariates than sample size can outperform simple ones. As our main contribution, we document the performance of high-dimensional synthetic control estimators with many control units. We find that adding control units can help improve imputation performance even beyond the point where the pre-treatment fit is perfect. We then provide a unified theoretical perspective on the performance of these high-dimensional models. Specifically, we show that more complex models can be interpreted as model-averaging estimators over simpler ones, which we link to an improvement in average performance. This perspective yields concrete insights into the use of synthetic control when control units are many relative to the number of pre-treatment periods.
    Date: 2023–05
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2305.00700&r=cmp
  21. By: Aayush Shah; Mann Doshi; Meet Parekh; Nirmit Deliwala; Prof. Pramila M. Chawan
    Abstract: The importance of predicting stock market prices cannot be overstated. It is a pivotal task for investors and financial institutions as it enables them to make informed investment decisions, manage risks, and ensure the stability of the financial system. Accurate stock market predictions can help investors maximize their returns and minimize their losses, while financial institutions can use this information to develop effective risk management policies. However, stock market prediction is a challenging task due to the complex nature of the stock market and the multitude of factors that can affect stock prices. As a result, advanced technologies such as deep learning are being increasingly utilized to analyze vast amounts of data and provide valuable insights into the behavior of the stock market. While deep learning has shown promise in accurately predicting stock prices, there is still much research to be done in this area.
    Date: 2023–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2304.09936&r=cmp
  22. By: Ali Lashgari
    Abstract: Forecasting financial time series (FTS) is an essential field in finance and economics that anticipates market movements in financial markets. This paper investigates the accuracy of text mining and technical analyses in forecasting financial time series. It focuses on the S&P500 stock market index during the pandemic, which tracks the performance of the largest publicly traded companies in the US. The study compares two methods of forecasting the future price of the S&P500: text mining, which uses NLP techniques to extract meaningful insights from financial news, and technical analysis, which uses historical price and volume data to make predictions. The study examines the advantages and limitations of both methods and analyze their performance in predicting the S&P500. The FinBERT model outperforms other models in terms of S&P500 price prediction, as evidenced by its lower RMSE value, and has the potential to revolutionize financial analysis and prediction using financial news data. Keywords: ARIMA, BERT, FinBERT, Forecasting Financial Time Series, GARCH, LSTM, Technical Analysis, Text Mining JEL classifications: G4, C8
    Date: 2023–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2304.14544&r=cmp

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