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on Financial Markets |
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Issue of 2024‒01‒29
nine papers chosen by |
| By: | Albert Dorador |
| Abstract: | We propose an alternative linearization to the classical Markowitz quadratic portfolio optimization model, based on maximum drawdown. This model, which minimizes maximum portfolio drawdown, is particularly appealing during times of financial distress, like during the COVID-19 pandemic. In addition, we will present a Mixed-Integer Linear Programming variation of our new model that, based on our out-of-sample results and sensitivity analysis, delivers a more profitable and robust solution with a 200 times faster solving time compared to the standard Markowitz quadratic formulation. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2401.02601&r=fmk |
| By: | Haochun Ma; Davide Prosperino; Alexander Haluszczynski; Christoph R\"ath |
| Abstract: | Identifying and quantifying co-dependence between financial instruments is a key challenge for researchers and practitioners in the financial industry. Linear measures such as the Pearson correlation are still widely used today, although their limited explanatory power is well known. In this paper we present a much more general framework for assessing co-dependencies by identifying and interpreting linear and nonlinear causalities in the complex system of financial markets. To do so, we use two different causal inference methods, transfer entropy and convergent cross-mapping, and employ Fourier transform surrogates to separate their linear and nonlinear contributions. We find that stock indices in Germany and the U.S. exhibit a significant degree of nonlinear causality and that correlation, while a very good proxy for linear causality, disregards nonlinear effects and hence underestimates causality itself. The presented framework enables the measurement of nonlinear causality, the correlation-causality fallacy, and motivates how causality can be used for inferring market signals, pair trading, and risk management of portfolios. Our results suggest that linear and nonlinear causality can be used as early warning indicators of abnormal market behavior, allowing for better trading strategies and risk management. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.16185&r=fmk |
| By: | Carletti, Elena; Leonello, Agnese; Marquez, Robert |
| Abstract: | Bank market power, both in the loan and deposit market, has important implications for credit provision and for financial stability. This article discusses these issues through the lens of a simple theoretical framework. On the asset side, banks choose the quality and quantity of loans. On the liability side, they may be subject to depositor runs whenever they offer demandable contracts. This structure allows us to review the literature on the role of market power for credit provision and stability and also highlight the interactions between the two sides of banks’ balance sheets. Our approach identifies relevant channels that deserve further analysis, especially given the rising importance of bank market power for monetay policy transmission and the the rise of the digital economy. JEL Classification: G01, G21, G28 |
| Keywords: | balance sheet interactions, bank runs, credit provision, digital economy, monetary policy transmission |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20242886&r=fmk |
| By: | Raffaele Giuseppe Cestari; Filippo Barchi; Riccardo Busetto; Daniele Marazzina; Simone Formentin |
| Abstract: | Accurately forecasting the direction of financial returns poses a formidable challenge, given the inherent unpredictability of financial time series. The task becomes even more arduous when applied to cryptocurrency returns, given the chaotic and intricately complex nature of crypto markets. In this study, we present a novel prediction algorithm using limit order book (LOB) data rooted in the Hawkes model, a category of point processes. Coupled with a continuous output error (COE) model, our approach offers a precise forecast of return signs by leveraging predictions of future financial interactions. Capitalizing on the non-uniformly sampled structure of the original time series, our strategy surpasses benchmark models in both prediction accuracy and cumulative profit when implemented in a trading environment. The efficacy of our approach is validated through Monte Carlo simulations across 50 scenarios. The research draws on LOB measurements from a centralized cryptocurrency exchange where the stablecoin Tether is exchanged against the U.S. dollar. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.16190&r=fmk |
| By: | Giulio Cornelli; Fiorella De Fiore; Leonardo Gambacorta; Cristina Manea |
| Abstract: | Fintech credit, which includes peer-to-peer and marketplace lending as well as lending facilitated by major technology firms, is witnessing rapid growth worldwide. However, its responsiveness to monetary policy shifts remains largely unexplored. This study employs a novel credit dataset spanning 19 countries from 2005 to 2020 and conducts a PVAR analysis to shed some light on the different reaction of fintech and bank credit to changes in policy rates. The main result is that fintech credit shows a lower (even non-significant) sensitivity to monetary policy shocks in comparison to traditional bank credit. Given the still marginal – although fast growing – macroeconomic significance of fintech credit, its contribution in explaining the variability of real GDP is less than 2%, against around one quarter for bank credit. |
| Keywords: | fintech credit, monetary policy, PVAR, collateral channel |
| JEL: | D22 G31 R30 |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:bis:biswps:1157&r=fmk |
| By: | Daube, Carl Heinz |
| Abstract: | The aim of this working paper is to provide a brief introduction to artificial intelligence and highlight specific potential applications in financial and investment decision-making. On the one hand, it is about where AI is already being used today in many areas of the financial industry. On the other hand, the aim is to show examples of what will be possible in the near future and where AI might lead to better, more sound decisions |
| Abstract: | Ziel dieses Working Papers ist es, eine kurze Einführung in die Künstliche Intelligenz zu geben und konkrete Einsatzmöglichkeiten in der Finanz- und Investitionsentscheidung aufzuzeigen. Dabei geht es zum einen darum, wo KI heute schon in vielen Bereichen der Finanzindustrie zum Einsatz kommt. Zum anderen geht es darum exemplarisch aufzuzeigen, was in naher Zukunft möglich sein wird und wo es auf der Basis von KI zu besseren, fundierteren Entscheidungen kommen könnte. |
| Keywords: | AI, Artificial Intelligence, investment decision, finance decision |
| JEL: | G00 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:esprep:280899&r=fmk |
| By: | Sahar Arshad; Seemab Latif; Ahmad Salman; Saadia Irfan |
| Abstract: | Financial forecasting plays an important role in making informed decisions for financial stakeholders, specifically in the stock exchange market. In a traditional setting, investors commonly rely on the equity research department for valuable reports on market insights and investment recommendations. The equity research department, however, faces challenges in effectuating decision-making due to the demanding cognitive effort required for analyzing the inherently volatile nature of market dynamics. Furthermore, financial forecasting systems employed by analysts pose potential risks in terms of interpretability and gaining the trust of all stakeholders. This paper presents an interpretable decision-making model leveraging the SHAP-based explainability technique to forecast investment recommendations. The proposed solution not only provides valuable insights into the factors that influence forecasted recommendations but also caters to investors of varying types, including those interested in daily and short-term investment opportunities. To ascertain the efficacy of the proposed model, a case study is devised that demonstrates a notable enhancement in investor's portfolio value, employing our trading strategies. The results highlight the significance of incorporating interpretability in forecasting models to boost stakeholders' confidence and foster transparency in the stock exchange domain. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.16223&r=fmk |
| By: | Irfan Cercil; Cem Ali Gökcen |
| Abstract: | Most advanced economy central banks cut their policy rates and introduced asset purchase programs (APPs) to weather the impacts of the Covid-19 pandemic on their economies and financial systems. Similar to their advanced economy counterparts, a number of emerging market (EM) central banks also initiated APPs during the Covid-19 pandemic. In this paper, we analyze the effects of these EM APPs on financial market variables such as sovereign bond yields, nominal exchange rates vis-à-vis US dollar, and stock market indices by using a novel causal inference approach. We utilize the local projections (LP) methodology of Jordà (2005) and estimate the average treatment effect (ATE) of EM APPs by applying the augmented inverse probability weighting (AIPW) estimator that addresses the selection bias and endogeneity problems inherent in the statistical analysis of quantitative easing (QE) policies. Our empirical findings suggest that QE policies adopted by EM central banks played an instrumental role in lowering sovereign bond yields and supporting exchange rates and equity markets during Covid-19 pandemic. This suggests that QE policies may complement traditional monetary policies in EM countries especially during periods of elevated market stress and uncertainty. |
| Keywords: | Covid-19, Quantitative easing, Asset purchase program, Central banks, Emerging markets, Local projections, Augmented inverse probability weighting estimation. |
| JEL: | E5 F3 G1 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:tcb:wpaper:2308&r=fmk |
| By: | Ingomar Krohn; Vladyslav Sushko; Witit Synsatayakul |
| Abstract: | This paper finds that trading by non-residents in an emerging financial market reinforces the existence of a momentum anomaly, in an apparent violation of an efficient market hypothesis. Using detailed order flow data in Thai foreign exchange, equity, and fixed income markets, we find that foreign investors engage in momentum trading, which amplifies positive feedback between returns and order flow across all asset classes. Innovations in foreign investor order flow are informative of future returns, but the information is not based on local macro fundamentals. Local financial investors tend to mimic foreign investor trading, reinforcing returns to momentum, while non-financial investors consistently provide liquidity. Further tests suggest that the returns to momentum trading are time-varying and are positively related to the amount of foreign capital flowing into the local financial market. Taken together, the results indicate that a significant presence of foreign investors can alter the trading behaviour of local investors and can reduce the importance of local fundamentals in driving asset prices. |
| Keywords: | international financial markets, heterogeneous trading, disaggregated order flow, foreign investors, emerging markets |
| JEL: | F30 G11 G14 G15 |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:bis:biswps:1154&r=fmk |