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on Financial Markets |
| By: | CJ Finnegan; James F. McCann; Salissou Moutari |
| Abstract: | In this paper we introduce a multi-agent deep-learning method which trades in the Futures markets based on the US S&P 500 index. The method (referred to as Model A) is an innovation founded on existing well-established machine-learning models which sample market prices and associated derivatives in order to decide whether the investment should be long/short or closed (zero exposure), on a day-to-day decision. We compare the predictions with some conventional machine-learning methods namely, Long Short-Term Memory, Random Forest and Gradient-Boosted-Trees. Results are benchmarked against a passive model in which the Futures contracts are held (long) continuously with the same exposure (level of investment). Historical tests are based on daily daytime trading carried out over a period of 6 calendar years (2018-23). We find that Model A outperforms the passive investment in key performance metrics, placing it within the top quartile performance of US Large Cap active fund managers. Model A also outperforms the three machine-learning classification comparators over this period. We observe that Model A is extremely efficient (doing less and getting more) with an exposure to the market of only 41.95% compared to the 100% market exposure of the passive investment, and thus provides increased profitability with reduced risk. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.11740 |
| By: | Andries, Marianne; Bianchi, Milo; Huynh, Karen; Pouget, Sébastien |
| Abstract: | In an investment experiment, we show variations in information affect belief and decision behaviors within the information-beliefs-decisions chain. Subjects observe the time series of a risky asset and a signal that, in random rounds, helps predict returns. When they perceive the signal as useless, subjects form extrapolative forecasts, and their investment decisions underreact to their beliefs. When they perceive the signal as predictive, the same subjects rationally use it in their forecasts, they no longer extrapolate, and they rely significantly more on their forecasts when making risk allocations. Analyzing investments without observing forecasts and information sets leads to erroneous interpretations. |
| Keywords: | Return Predictability, Expectations, Long-Term Investment, Extrapolation, Model Uncertainty. |
| JEL: | G11 G41 D84 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:tse:wpaper:129666 |
| By: | Dimitar Kitanovski; Igor Mishkovski; Viktor Stojkoski; Miroslav Mirchev |
| Abstract: | Maintaining a balance between returns and volatility is a common strategy for portfolio diversification, whether investing in traditional equities or digital assets like cryptocurrencies. One approach for diversification is the application of community detection or clustering, using a network representing the relationships between assets. We examine two network representations, one based on a standard distance matrix based on correlation, and another based on mutual information. The Louvain and Affinity propagation algorithms were employed for finding the network communities (clusters) based on annual data. Furthermore, we examine building assets' co-occurrence networks, where communities are detected for each month throughout a whole year and then the links represent how often assets belong to the same community. Portfolios are then constructed by selecting several assets from each community based on local properties (degree centrality), global properties (closeness centrality), or explained variance (Principal component analysis), with three value ranges (max, med, min), calculated on a maximal spanning tree or a fully connected community sub-graph. We explored these various strategies on data from the S\&P 500 and the Top 203 cryptocurrencies with a market cap above 2M USD in the period from Jan 2019 to Sep 2022. Moreover, we study into more details the periods of the beginning of the COVID-19 outbreak and the start of the war in Ukraine. The results confirm some of the previous findings already known for traditional stock markets and provide some further insights, while they reveal an opposing trend in the crypto-assets market. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.11739 |
| By: | Tobias Berg; Jan Keil; Felix Martini; Manju Puri |
| Abstract: | We analyze the effect of a major central bank digital currency (CBDC) – the digital euro – on the payment industry to find remarkably heterogeneous effects. Stock prices of U.S. payment firms decrease, while stock prices of European payment firms increase in response to positive announcements on the digital euro. Bank stocks do not react. We estimate a loss in market capitalization of USD 127 billion for U.S. payment firms, vis-à-vis a gain of USD 23 billion for European payment firms. Our results emphasize the medium-of-exchange function of CBDCs and point to a novel geopolitical dimension of CBDCs: enhanced autonomy in payments. |
| JEL: | G1 G20 G21 G22 G23 G24 G28 G29 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32857 |
| By: | Stefan Nagel; Zhengyang Xu |
| Abstract: | We show that the stock market price reaction to monetary policy surprises upon announcements of the Federal Open Market Committee (FOMC) is explained mostly by changes in the default-free term structure of yields, not by changes in the equity premium. We reach this conclusion based on a new model-free method that uses dividend futures prices to obtain the counterfactual stock market index price change that results purely from the change in the default-free yield curve induced by the monetary policy surprise. The yield curve change in turn partly reflects a change in expected future short-term interest rates, as measured by changes in professional forecasts, and partly a change in the term premium. We further find that the even/odd week FOMC cycle in stock index returns is also largely due to an FOMC cycle in the yield curve rather than the equity premium. |
| JEL: | E52 G12 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32884 |
| By: | Sid Bhatia; Sidharth Peri; Sam Friedman; Michelle Malen |
| Abstract: | This research presents a comprehensive framework for analyzing liquidity in financial markets, particularly in the context of high-frequency trading. By leveraging advanced machine learning classification techniques, including Logistic Regression, Support Vector Machine, and Random Forest, the study aims to predict minute-level price movements using an extensive set of liquidity metrics derived from the Trade and Quote (TAQ) data. The findings reveal that employing a broad spectrum of liquidity measures yields higher predictive accuracy compared to models utilizing a reduced subset of features. Key liquidity metrics, such as Liquidity Ratio, Flow Ratio, and Turnover, consistently emerged as significant predictors across all models, with the Random Forest algorithm demonstrating superior accuracy. This study not only underscores the critical role of liquidity in market stability and transaction costs but also highlights the complexities involved in short-interval market predictions. The research suggests that a comprehensive set of liquidity measures is essential for accurate prediction, and proposes future work to validate these findings across different stock datasets to assess their generalizability. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.10016 |