nep-fmk New Economics Papers
on Financial Markets
Issue of 2022‒12‒05
fourteen papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Stock price reaction to power outages following extreme weather events: Evidence from Texas power outage By Sherry Hu; Kose John; Balbinder Singh Gill
  2. The spectre of terrorism and the stock market By Hanna, Alan J.; Turner, John D.; Walker, Clive B.
  3. Evaluating Impact of Social Media Posts by Executives on Stock Prices By Anubhav Sarkar; Swagata Chakraborty; Sohom Ghosh; Sudip Kumar Naskar
  4. Who creates and who bears flow externalities in mutual funds? By Fricke, Daniel; Jank, Stephan; Wilke, Hannes
  5. Demand Segmentation in the Federal Funds Market By Manjola Tase
  6. Stock Trading Volume Prediction with Dual-Process Meta-Learning By Ruibo Chen; Wei Li; Zhiyuan Zhang; Ruihan Bao; Keiko Harimoto; Xu Sun
  7. Uncertainty Aware Trader-Company Method: Interpretable Stock Price Prediction Capturing Uncertainty By Yugo Fujimotol; Kei Nakagawa; Kentaro Imajo; Kentaro Minami
  8. Bitcoin flash crash on May 19, 2021: What did really happen on Binance? By Baumgartner, Tim; Güttler, André
  9. Ownership Diversification and Product Market Pricing Incentives By Albert Banal-Estañol; Jo Seldeslachts; Xavier Vives
  10. Predictive Crypto-Asset Automated Market Making Architecture for Decentralized Finance using Deep Reinforcement Learning By Tristan Lim
  11. Investment Portfolio Optimization Based on Modern Portfolio Theory and Deep Learning Models By Maciej Wysocki; Paweł Sakowski
  12. Monitoring the Dynamic Networks of Stock Returns By Elena Farahbakhsh Touli; Hoang Nguyen; Olha Bodnar
  13. Daily and intraday application of various architectures of the LSTM model in algorithmic investment strategies on Bitcoin and the S&P 500 Index By Katarzyna Kryńska; Robert Ślepaczuk
  14. Crypto trading and Bitcoin prices: evidence from a new database of retail adoption By Raphael Auer; Giulio Cornelli; Sebastian Doerr; Jon Frost; Leonardo Gambacorta

  1. By: Sherry Hu; Kose John; Balbinder Singh Gill
    Abstract: In this study, we evaluate the effects of natural disasters on the stock (market) values of firms located in the affected counties. We are able to measure the change in stock prices of the firms affected by the 2021 Texas winter storm. To measure the abnormal return due to the storm, we use four different benchmark models: (1) the market-adjusted model, (2) the market model, (3) the Fama-French three-factor model, and (4) the Fama French plus momentum model. These statistical models in finance characterize the normal risk-return trade-off.
    Date: 2022–10
  2. By: Hanna, Alan J.; Turner, John D.; Walker, Clive B.
    Abstract: Terrorism is a major issue in the 21st century. In this paper we examine the effect of terrorism on the stock market. We go beyond previous studies to explore the spectre of terrorism on the market rather than terrorist activities. Using a narrative-based approach à la Shiller (2019), we find that the spectre of terrorism during the Northern Ireland Troubles reduced returns and increased volatility on the UK stock market.
    Keywords: terrorism,stock market,returns,volatility,narratives
    JEL: C00 E44 G12 G40 N24
    Date: 2022
  3. By: Anubhav Sarkar; Swagata Chakraborty; Sohom Ghosh; Sudip Kumar Naskar
    Abstract: Predicting stock market movements has always been of great interest to investors and an active area of research. Research has proven that popularity of products is highly influenced by what people talk about. Social media like Twitter, Reddit have become hotspots of such influences. This paper investigates the impact of social media posts on close price prediction of stocks using Twitter and Reddit posts. Our objective is to integrate sentiment of social media data with historical stock data and study its effect on closing prices using time series models. We carried out rigorous experiments and deep analysis using multiple deep learning based models on different datasets to study the influence of posts by executives and general people on the close price. Experimental results on multiple stocks (Apple and Tesla) and decentralised currencies (Bitcoin and Ethereum) consistently show improvements in prediction on including social media data and greater improvements on including executive posts.
    Date: 2022–10
  4. By: Fricke, Daniel; Jank, Stephan; Wilke, Hannes
    Abstract: Using a unique dataset on the sectoral ownership structure of euro area equity mutual funds, we study how different investor groups contribute to the negative performance externality from large outflows. Investment funds, as holders of mutual funds, are the main contributors to the flow externality. Insurers and households, in particular less financially-sophisticated ones, are the main receivers. These differences are due to investment funds reacting more strongly on past performance and displaying a more procyclical investment behavior compared to households and insurers. Our results raise consumer protection and financial stability concerns due to the trading activity of short-term oriented investors.
    Keywords: asset management,mutual funds,externalities,contagion,performance
    JEL: G10 G11 G23
    Date: 2022
  5. By: Manjola Tase
    Abstract: This paper outlines a model of demand segmentation in the federal funds market with two types of borrowers - the "interest on reserves (IOR) arbitrage'' type and the "regulatory'' type - which have different reservation prices and cannot always be separated. When fed funds trade above IOR, the "regulatory" type is revealed and consequently pays an interest rate closer to its real reservation price, pushing the fed funds rate further up. When fed funds trade below IOR, a decrease in the fed funds rate encourages entry in the market for IOR arbitrage purposes thus counteracting the downward pressure on the fed funds rate. We use probit regression models and daily data for the period April 2018 to February 2020 to provide empirical support for this model. We find the following: 1) When fed funds trade above IOR, there is, on average, a 10 percentage points increase in the probability that the fed funds rate increases the following period. Furthermore, analysis using confidential bank-level data shows that this increase in the probability is higher for banks that report their liquidity profile daily and that were present all trading days during this period. 2) When the fed funds trade below IOR, the probability of a decrease in the fed funds rate decreases with the widening of the spread between the fed funds rate and IOR.
    Keywords: Fed funds; Demand segmentation; Repo; Monetary policy
    JEL: E49 E52 G28
    Date: 2022–11–04
  6. By: Ruibo Chen; Wei Li; Zhiyuan Zhang; Ruihan Bao; Keiko Harimoto; Xu Sun
    Abstract: Volume prediction is one of the fundamental objectives in the Fintech area, which is helpful for many downstream tasks, e.g., algorithmic trading. Previous methods mostly learn a universal model for different stocks. However, this kind of practice omits the specific characteristics of individual stocks by applying the same set of parameters for different stocks. On the other hand, learning different models for each stock would face data sparsity or cold start problems for many stocks with small capitalization. To take advantage of the data scale and the various characteristics of individual stocks, we propose a dual-process meta-learning method that treats the prediction of each stock as one task under the meta-learning framework. Our method can model the common pattern behind different stocks with a meta-learner, while modeling the specific pattern for each stock across time spans with stock-dependent parameters. Furthermore, we propose to mine the pattern of each stock in the form of a latent variable which is then used for learning the parameters for the prediction module. This makes the prediction procedure aware of the data pattern. Extensive experiments on volume predictions show that our method can improve the performance of various baseline models. Further analyses testify the effectiveness of our proposed meta-learning framework.
    Date: 2022–10
  7. By: Yugo Fujimotol; Kei Nakagawa; Kentaro Imajo; Kentaro Minami
    Abstract: Machine learning is an increasingly popular tool with some success in predicting stock prices. One promising method is the Trader-Company~(TC) method, which takes into account the dynamism of the stock market and has both high predictive power and interpretability. Machine learning-based stock prediction methods including the TC method have been concentrating on point prediction. However, point prediction in the absence of uncertainty estimates lacks credibility quantification and raises concerns about safety. The challenge in this paper is to make an investment strategy that combines high predictive power and the ability to quantify uncertainty. We propose a novel approach called Uncertainty Aware Trader-Company Method~(UTC) method. The core idea of this approach is to combine the strengths of both frameworks by merging the TC method with the probabilistic modeling, which provides probabilistic predictions and uncertainty estimations. We expect this to retain the predictive power and interpretability of the TC method while capturing the uncertainty. We theoretically prove that the proposed method estimates the posterior variance and does not introduce additional biases from the original TC method. We conduct a comprehensive evaluation of our approach based on the synthetic and real market datasets. We confirm with synthetic data that the UTC method can detect situations where the uncertainty increases and the prediction is difficult. We also confirmed that the UTC method can detect abrupt changes in data generating distributions. We demonstrate with real market data that the UTC method can achieve higher returns and lower risks than baselines.
    Date: 2022–10
  8. By: Baumgartner, Tim; Güttler, André
    Abstract: Bitcoin plunged by 30% on May 19, 2021. We examine the outage the largest crypto exchange Binance experienced during the crash, when it halted trading for retail clients and stopped providing transaction data. We find evidence that Binance back-filled these missing transactions with data that does not conform to Benford's Law. The Bitcoin futures price difference between Binance and other exchanges was seven times larger during the crash period compared to a prior reference period. Data manipulation is a plausible explanation for our findings. These actions are in line with Binance aiming to limit losses for its futures-related insurance fund.
    Keywords: Benford's law,Binance,Bitcoin,cryptocurrency,crypto exchange,derivatives,extreme volatility,fraud,market crash,trading outage
    JEL: G10 G12 G14 K22
    Date: 2022
  9. By: Albert Banal-Estañol; Jo Seldeslachts; Xavier Vives
    Abstract: We link investor ownership to profit loads on rival firms by the managers of a firm. We propose a theory model in which we distinguish between passive and active investors’ holdings, where passive investors are relatively more diversified. We find that if passive investors become relatively bigger, then common ownership incentives increase. We show that these higher incentives, in turn, are linked to higher firm markups. We empirically confirm these relationships for public US firms in the years 2004-2012, where the financial crisis coincides with passive investors’ rise. The found effects are small but non-negligible.
    Date: 2022–11
  10. By: Tristan Lim
    Abstract: The study proposes a quote-driven predictive automated market maker (AMM) platform with on-chain custody and settlement functions, alongside off-chain predictive reinforcement learning capabilities to improve liquidity provision of real-world AMMs. The proposed AMM architecture is an augmentation to the Uniswap V3, a cryptocurrency AMM protocol, by utilizing a novel market equilibrium pricing for reduced divergence and slippage loss. Further, the proposed architecture involves a predictive AMM capability, utilizing a deep hybrid Long Short-Term Memory (LSTM) and Q-learning reinforcement learning framework that looks to improve market efficiency through better forecasts of liquidity concentration ranges, so liquidity starts moving to expected concentration ranges, prior to asset price movement, so that liquidity utilization is improved. The augmented protocol framework is expected have practical real-world implications, by (i) reducing divergence loss for liquidity providers, (ii) reducing slippage for crypto-asset traders, while (iii) improving capital efficiency for liquidity provision for the AMM protocol. To our best knowledge, there are no known protocol or literature that are proposing similar deep learning-augmented AMM that achieves similar capital efficiency and loss minimization objectives for practical real-world applications.
    Date: 2022–09
  11. By: Maciej Wysocki (University of Warsaw, Faculty of Economic Sciences; Quantitative Finance Research Group); Paweł Sakowski (University of Warsaw, Faculty of Economic Sciences; Quantitative Finance Research Group)
    Abstract: This paper investigates an important problem of an appropriate variance-covariance matrix estimation in the Modern Portfolio Theory. In this study we propose a novel framework for variance-covariance matrix estimation for purposes of the portfolio optimization, which is based on deep learning models. We employ the long short-term memory (LSTM) recurrent neural networks (RNN) along with two probabilistic deep learning models: DeepVAR and GPVAR to the task of one-day ahead multivariate forecasting. We then use these forecasts to optimize portfolios that consisted of stocks and cryptocurrencies. Our analysis presents results across different combinations of observation windows and rebalancing periods to compare performances of classical and deep learning variance-covariance estimation methods. The conclusions of the study are that although the strategies (portfolios) performance differed significantly between different combinations of parameters, generally the best results in terms of the information ratio and annualized returns are obtained using the LSTM-RNN models. Moreover, longer observation windows translate into better performance of the deep learning models indicating that these methods require longer windows to be able to efficiently capture the long-term dependencies of the variance-covariance matrix structure. Strategies with less frequent rebalancing typically perform better than these with the shortest rebalancing windows across all considered methods.
    Keywords: Portfolio Optimization, Deep Learning, Variance-Covariance Matrix Forecasting, Investment Strategies, Recurrent Neural Networks, Long Short-Term Memory Neural Networks
    JEL: C4 C14 C45 C53 C58 G11
    Date: 2022
  12. By: Elena Farahbakhsh Touli; Hoang Nguyen; Olha Bodnar
    Abstract: In this paper, we study the connection between the companies in the Swedish capital market. We consider 28 companies included in the determination of the market index OMX30. The network structure of the market is constructed using different methods to determine the distance between the companies. We use hierarchical clustering methods to find the relation among the companies in each window. Next, we obtain one-dimensional time series of the distances between the clustering trees that reflect the changes in the relationship between the companies in the market over time. The method of statistical process control, namely the Shewhart control chart, is applied to those time series to detect abnormal changes in the financial market.
    Date: 2022–10
  13. By: Katarzyna Kryńska (University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group); Robert Ślepaczuk (University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group, Department of Quantitative Finance)
    Abstract: This thesis investigates the use of various architectures of the LSTM model in algorithmic investment strategies. LSTM models are used to generate buy/sell signals, with previous levels of Bitcoin price and the S&P 500 Index value as inputs. Four approaches are tested: two are regression problems (price level prediction) and the other two are classification problems (prediction of price direction). All approaches are applied to daily, hourly, and 15-minute data and are using a walk-forward optimization procedure. The out-of-sample period for the S&P 500 Index is from February 6, 2014 to November 27, 2020, and for Bitcoin it is from January 15, 2014 to December 1, 2020. We discover that classification techniques beat regression methods on average, but we cannot determine if intra-day models outperform inter-day models. We come to the conclusion that the ensembling of models does not always have a positive impact on performance. Finally, a sensitivity analysis is performed to determine how changes in the main hyperparameters of the LSTM model affect strategy performance.
    Keywords: machine learning, deep learning, recurrent neural networks, LSTM, algorithmic trading, ensemble investment strategy, intra-day trading, S&P 500 Index, Bitcoin
    JEL: C4 C14 C45 C53 C58 G13
    Date: 2022
  14. By: Raphael Auer; Giulio Cornelli; Sebastian Doerr; Jon Frost; Leonardo Gambacorta
    Abstract: Prices for cryptocurrencies have undergone multiple boom-bust cycles, together with ongoing entry by retail investors. To investigate the drivers of crypto adoption, we assemble a novel database (made available with this paper) on retail use of crypto exchange apps at daily frequency for 95 countries over 2015–22. We show that a rising Bitcoin price is followed by the entry of new users. About 40% of these new users are men under 35, commonly identified as the most "risk-seeking" segment of the population. To establish a causal effect of prices on adoption, we exploit two exogenous shocks: the crackdown of Chinese authorities on crypto mining in mid2021 and the social unrest in Kazakhstan in early 2022. During both episodes price changes have a significant effect on the entry of new users. Results from a PVAR model corroborate these findings. Overall, back of the envelope calculations suggest that around three-quarters of users have lost money on their Bitcoin investments.
    Keywords: bitcoin, cryptocurrencies, cryptoassets, regulation, decentralised finance, DeFi, retail investment.
    JEL: E42 E51 E58 F31 G28 L50 O32
    Date: 2022–11

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