nep-fmk New Economics Papers
on Financial Markets
Issue of 2020‒03‒16
five papers chosen by

  1. Machine Learning Portfolio Allocation By Michael Pinelis; David Ruppert
  2. Ascertaining price formation in cryptocurrency markets with DeepLearning By Fan Fang; Waichung Chung; Carmine Ventre; Michail Basios; Leslie Kanthan; Lingbo Li; Fan Wu
  3. Intermediation in the Interbank Lending Market By Ben R. Craig; Yiming Ma
  4. The Stock Market Reaction to Mergers and Acquisitions: Evidence from the Banking Industry By Juan M. Lozada; Lina M. Cortés; Daniel Velasquez Gaviria
  5. Stock Return and Risk Premium: Evidence from Turkey By Tursoy, Turgut; Berk, Niyazi

  1. By: Michael Pinelis; David Ruppert
    Abstract: We find economically and statistically significant gains from using machine learning to dynamically allocate between the market index and the risk-free asset. We model the market price of risk to determine the optimal weights in the portfolio: reward-risk market timing. This involves forecasting the direction of next month's excess return, which gives the reward, and constructing a dynamic volatility estimator that is optimized with a machine learning model, which gives the risk. Reward-risk timing with machine learning provides substantial improvements in investor utility, alphas, Sharpe ratios, and maximum drawdowns, after accounting for transaction costs, leverage constraints, and on a new out-of-sample test set. This paper provides a unifying framework for machine learning applied to both return- and volatility-timing.
    Date: 2020–03
  2. By: Fan Fang; Waichung Chung; Carmine Ventre; Michail Basios; Leslie Kanthan; Lingbo Li; Fan Wu
    Abstract: The cryptocurrency market is amongst the fastest-growing of all the financial markets in the world. Unlike traditional markets, such as equities, foreign exchange and commodities, cryptocurrency market is considered to have larger volatility and illiquidity. This paper is inspired by the recent success of using deep learning for stock market prediction. In this work, we analyze and present the characteristics of the cryptocurrency market in a high-frequency setting. In particular, we applied a deep learning approach to predict the direction of the mid-price changes on the upcoming tick. We monitored live tick-level data from $8$ cryptocurrency pairs and applied both statistical and machine learning techniques to provide a live prediction. We reveal that promising results are possible for cryptocurrencies, and in particular, we achieve a consistent $78\%$ accuracy on the prediction of the mid-price movement on live exchange rate of Bitcoins vs US dollars.
    Date: 2020–02
  3. By: Ben R. Craig (Indiana University Bloomington; Federal Reserve Bank; Deutsche Bundesbank); Yiming Ma
    Abstract: This paper studies systemic risk in the interbank market. We first establish that in the German interbank lending market, a few large banks intermediate funding flows between many smaller periphery banks and that shocks to these intermediary banks in the financial crisis spill over to the activities of the periphery banks. We then develop a network model in which banks trade off the costs and benefits of link formation to explain these patterns. The model is structurally estimated using banks’ preferences as revealed by the observed network structure in the precrisis period. It explains why the interbank intermediation arrangement arises, estimates the frictions underlying the arrangement, and quantifies how shocks are transmitted across the network. Model estimates based on precrisis data successfully predict changes in network-links and in lending arising from the crisis in out-of-sample tests. Finally, we quantify the systemic risk of a single intermediary and the impact of ECB funding in reducing this risk through model counterfactuals.
    Date: 2020–03–05
  4. By: Juan M. Lozada; Lina M. Cortés; Daniel Velasquez Gaviria
    JEL: C32 G14 G21 G34
    Date: 2020–02–19
  5. By: Tursoy, Turgut; Berk, Niyazi
    Abstract: The finance theory suggests that there might be a relationship between the stock return and the risk premium. Theoretically, stock return defined as the change of the market price, and it is related to the scope of the financial system, which is consisting of the financial institution and financial markets. The way, possibly will be, to contribute the existing literature is to propose a new measurement and this study try to do so. The aim of this study and its motivation is that investigates a new measure of stock return and attempt to establish a new relationship between return and risk premium. To realize this aim, this study uses geometric mean to calculate return and standard deviation, and after all, construct panel data analysis to analyze the return and standard deviation relationship. In this study, seven commercial banks’ data analyzed to the relationship between return and standard deviation with panel data analyses between 1991 and 2010. Also, the geometric mean and value relative concept used to estimate return and the monthly stock prices to yearly basis.
    Keywords: Asset Pricing, Stock Return, Risk
    JEL: G11 G12 G21
    Date: 2020–03–01

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NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.