nep-fmk New Economics Papers
on Financial Markets
Issue of 2021‒04‒12
ten papers chosen by

  1. Effect of Market-Wide Herding on the Next Day's Stock Return By Andrey Kudryavtsev
  2. Accurate Stock Price Forecasting Using Robust and Optimized Deep Learning Models By Jaydip Sen; Sidra Mehtab
  3. Forecasting with Deep Learning: S&P 500 index By Firuz Kamalov; Linda Smail; Ikhlaas Gurrib
  4. The VIX index under scrutiny of machine learning techniques and neural networks By Ali Hirsa; Joerg Osterrieder; Branka Hadji Misheva; Wenxin Cao; Yiwen Fu; Hanze Sun; Kin Wai Wong
  5. Pyramid scheme in stock market: a kind of financial market simulation By Yong Shi; Bo Li; Guangle Du
  6. The Rise of Dual-Class Stock IPOs By Dhruv Aggarwal; Ofer Eldar; Yael Hochberg; Lubomir P. Litov
  7. Linking Community Banks and Fintech Platforms By Patrick T. Harker
  8. Research on Portfolio Liquidation Strategy under Discrete Times By Qixuan Luo; Yu Shi; Handong Li
  9. Big Data in Finance By Itay Goldstein; Chester S. Spatt; Mao Ye
  10. Measuring Systemic Risk in South African Banks By Somnath Chatterjee; Marea Sing

  1. By: Andrey Kudryavtsev (Economics and Management Department, The Max Stern Yezreel Valley Academic College, Emek Yezreel 19300, Israel)
    Abstract: The study analyzes daily cross-sectional market-wide herd behavior as a potential factor that may help in predicting next day's stock returns. Assuming that herding may lead to stock price overreaction and result in subsequent price reversals, I suggest that for a given stock, daily returns should be higher (lower) following trading days characterized by negative (positive) stock's returns and high levels of herd behavior. Analyzing daily price data for all the constituents of S&P 500 Index over the period from 1993 to 2019, and using two alternative market-wide herding measures, I document that following trading days characterized by high levels of herding, stock returns tend to exhibit significant reversals, while following trading days characterized by low levels of herding, stock returns tend to exhibit significant drifts.This effect is found to be more pronounced for smaller and more volatile stocks. Based on the study's findings, I formulate a trading strategy and demonstrate that it yields significantly positive returns.
    Keywords: Behavioral Finance, herd behavior, herding, stock price drifts, stock price reversals, trading strategy.
    JEL: G11 G14 G19
    Date: 2021–03
  2. By: Jaydip Sen; Sidra Mehtab
    Abstract: Designing robust frameworks for precise prediction of future prices of stocks has always been considered a very challenging research problem. The advocates of the classical efficient market hypothesis affirm that it is impossible to accurately predict the future prices in an efficiently operating market due to the stochastic nature of the stock price variables. However, numerous propositions exist in the literature with varying degrees of sophistication and complexity that illustrate how algorithms and models can be designed for making efficient, accurate, and robust predictions of stock prices. We present a gamut of ten deep learning models of regression for precise and robust prediction of the future prices of the stock of a critical company in the auto sector of India. Using a very granular stock price collected at 5 minutes intervals, we train the models based on the records from 31st Dec, 2012 to 27th Dec, 2013. The testing of the models is done using records from 30th Dec, 2013 to 9th Jan 2015. We explain the design principles of the models and analyze the results of their performance based on accuracy in forecasting and speed of execution.
    Date: 2021–03
  3. By: Firuz Kamalov; Linda Smail; Ikhlaas Gurrib
    Abstract: Stock price prediction has been the focus of a large amount of research but an acceptable solution has so far escaped academics. Recent advances in deep learning have motivated researchers to apply neural networks to stock prediction. In this paper, we propose a convolution-based neural network model for predicting the future value of the S&P 500 index. The proposed model is capable of predicting the next-day direction of the index based on the previous values of the index. Experiments show that our model outperforms a number of benchmarks achieving an accuracy rate of over 55%.
    Date: 2021–03
  4. By: Ali Hirsa; Joerg Osterrieder; Branka Hadji Misheva; Wenxin Cao; Yiwen Fu; Hanze Sun; Kin Wai Wong
    Abstract: The CBOE Volatility Index, known by its ticker symbol VIX, is a popular measure of the market's expected volatility on the SP 500 Index, calculated and published by the Chicago Board Options Exchange (CBOE). It is also often referred to as the fear index or the fear gauge. The current VIX index value quotes the expected annualized change in the SP 500 index over the following 30 days, based on options-based theory and current options-market data. Despite its theoretical foundation in option price theory, CBOE's Volatility Index is prone to inadvertent and deliberate errors because it is weighted average of out-of-the-money calls and puts which could be illiquid. Many claims of market manipulation have been brought up against VIX in recent years. This paper discusses several approaches to replicate the VIX index as well as VIX futures by using a subset of relevant options as well as neural networks that are trained to automatically learn the underlying formula. Using subset selection approaches on top of the original CBOE methodology, as well as building machine learning and neural network models including Random Forests, Support Vector Machines, feed-forward neural networks, and long short-term memory (LSTM) models, we will show that a small number of options is sufficient to replicate the VIX index. Once we are able to actually replicate the VIX using a small number of SP options we will be able to exploit potential arbitrage opportunities between the VIX index and its underlying derivatives. The results are supposed to help investors to better understand the options market, and more importantly, to give guidance to the US regulators and CBOE that have been investigating those manipulation claims for several years.
    Date: 2021–02
  5. By: Yong Shi; Bo Li; Guangle Du
    Abstract: Artificial stock market simulation based on agent is an important means to study financial market. Based on the assumption that the investors are composed of a main fund, small trend and contrarian investors characterized by four parameters, we simulate and research a kind of financial phenomenon with the characteristics of pyramid schemes. Our simulation results and theoretical analysis reveal the relationships between the rate of return of the main fund and the proportion of the trend investors in all small investors, the small investors' parameters of taking profit and stopping loss, the order size of the main fund and the strategies adopted by the main fund. Our work are helpful to explain the financial phenomenon with the characteristics of pyramid schemes in financial markets, design trading rules for regulators and develop trading strategies for investors.
    Date: 2021–02
  6. By: Dhruv Aggarwal; Ofer Eldar; Yael Hochberg; Lubomir P. Litov
    Abstract: We create a novel dataset to examine the nature and determinants of dual-class IPOs. We document that dual-class firms have different types of controlling shareholders and wedges between voting and economic rights. We find that the founders' wedge is largest when founders have stronger bargaining power. The increase in founder wedge over time is due to increased willingness by venture capitalists to accommodate founder control and technological shocks that reduced firms' needs for external financing. Greater founder bargaining power is also associated with a lower likelihood of sunset provisions that eliminate dual-class structures within specified periods.
    JEL: G24 G28 G34
    Date: 2021–03
  7. By: Patrick T. Harker
    Abstract: Speaking at the Joint Virtual Fintech Partnership Symposium, Philadelphia Fed President Patrick Harker stressed the vitally important role community banks play in the economy and noted the risks smaller banks face in adopting new technologies. Hosted by the Federal Reserve Bank of Philadelphia and the Conference of State Bank Supervisors, the event highlighted fintech options for community banks and credit unions.
    Date: 2021–04–01
  8. By: Qixuan Luo; Yu Shi; Handong Li
    Abstract: This paper presents an optimal strategy for portfolio liquidation under discrete time conditions. We assume that N risky assets held will be liquidated according to the same time interval and order quantity, and the basic price processes of assets are generated by an N-dimensional independent standard Brownian motion. The permanent impact generated by an asset in the portfolio during the liquidation will affect all assets, and the temporary impact generated by one asset will only affect itself. On this basis, we establish a liquidation cost model based on the VaR measurement and obtain an optimal liquidation time under discrete-time conditions. The optimal solution shows that the liquidation time is only related to the temporary impact rather than the permanent impact. In the simulation analysis, we give the relationship between volatility parameters, temporary price impact and the optimal liquidation strategy.
    Date: 2021–03
  9. By: Itay Goldstein; Chester S. Spatt; Mao Ye
    Abstract: Big data is revolutionizing the finance industry and has the potential to significantly shape future research in finance. This special issue contains articles following the 2019 NBER/ RFS conference on big data. In this Introduction to the special issue, we define the “Big Data” phenomenon as a combination of three features: large size, high dimension, and complex structure. Using the articles in the special issue, we discuss how new research builds on these features to push the frontier on fundamental questions across areas in finance – including corporate finance, market microstructure, and asset pricing. Finally, we offer some thoughts for future research directions.
    JEL: G12 G14 G3
    Date: 2021–03
  10. By: Somnath Chatterjee; Marea Sing
    Abstract: MeasuringSystemicRiskinSouthAfricanBanks
    Date: 2021–04–06

General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.