nep-fmk New Economics Papers
on Financial Markets
Issue of 2020‒11‒09
eleven papers chosen by

  1. Treasury Market Liquidity and Early Lessons from the Pandemic Shock By Lorie Logan
  2. Monetary policy effects in times of negative interest rates: What do bank stock prices tell us? By Joost Bats; Massimo Giuliodori; Aerdt Houben
  3. Deep Reinforcement Learning for Asset Allocation in US Equities By Miquel Noguer i Alonso; Sonam Srivastava
  4. Bear, Bull, Sidewalk, and Crash: The Evolution of the US Stock Market Using Over a Century of Daily Data By Shixuan Wang; Rangan Gupta; Yue-Jun Zhang
  5. Global effects of US uncertainty: real and financial shocks on real and financial markets By Gomez-Gonzalez, Jose Eduardo; Hirs-Garzon, Jorge; Uribe, Jorge M.
  6. Time-Varying Risk Aversion and Forecastability of the US Term Structure of Interest Rates By Elie Bouri; Rangan Gupta; Anandamayee Majumdar; Sowmya Subramaniam
  7. The Stock Market–Real Economy "Disconnect": A Closer Look By Andrew Y. Chen; Markus F. Ibert; Francisco Vazquez-Grande
  8. Sovereign Bond Spreads and Credit Sensitivity By Ricardo Schefer
  9. Liquidity and Volatility By Itamar Drechsler; Alan Moreira; Alexi Savov
  10. When Bots Take Over the Stock Market: Evasion Attacks Against Algorithmic Traders By Elior Nehemya; Yael Mathov; Asaf Shabtai; Yuval Elovici
  11. Analysis of the impact of maker-taker fees on the stock market using agent-based simulation By Isao Yagi; Mahiro Hoshino; Takanobu Mizuta

  1. By: Lorie Logan
    Abstract: Remarks at Brookings-Chicago Booth Task Force on Financial Stability (TFFS) meeting, panel on market liquidity (delivered via videoconference).
    Keywords: treasury market; securities; liquidity; trading desk; dealers; finance; intermediation; participants; The Desk; Federal Reserve
    Date: 2020–10–23
  2. By: Joost Bats; Massimo Giuliodori; Aerdt Houben
    Abstract: Do negative interest rates matter for bank performance? This paper investigates whether monetary policy surprises impact bank stock prices differently in times of positive and negative interest rates. The analysis controls for broad stock market movements and finds that an unanticipated downward shift in the yield curve and a flattening of the shorter-end of the yield curve resulting from monetary policy announcements reduce bank stock prices in a low and especially negative interest rate environment. The effects persist in the days after the monetary policy announcement and are larger for banks relatively dependent on deposit funding. By contrast, a surprise movement in the slope of the longer-end of the yield curve does not impact bank stock prices in a negative interest rate environment. The results indicate that when market interest rates are negative but deposit rates stuck at zero, monetary policy instruments that target the longer-end of the yield curve are less detrimental to bank performance than those that target the shorter-end of the yield curve.
    Keywords: Monetary policy; bank stock prices; negative interest rates
    JEL: E43 E44 E52 G12 G21
    Date: 2020–10
  3. By: Miquel Noguer i Alonso; Sonam Srivastava
    Abstract: Reinforcement learning is a machine learning approach concerned with solving dynamic optimization problems in an almost model-free way by maximizing a reward function in state and action spaces. This property makes it an exciting area of research for financial problems. Asset allocation, where the goal is to obtain the weights of the assets that maximize the rewards in a given state of the market considering risk and transaction costs, is a problem easily framed using a reinforcement learning framework. It is first a prediction problem for expected returns and covariance matrix and then an optimization problem for returns, risk, and market impact. Investors and financial researchers have been working with approaches like mean-variance optimization, minimum variance, risk parity, and equally weighted and several methods to make expected returns and covariance matrices' predictions more robust. This paper demonstrates the application of reinforcement learning to create a financial model-free solution to the asset allocation problem, learning to solve the problem using time series and deep neural networks. We demonstrate this on daily data for the top 24 stocks in the US equities universe with daily rebalancing. We use a deep reinforcement model on US stocks using different architectures. We use Long Short Term Memory networks, Convolutional Neural Networks, and Recurrent Neural Networks and compare them with more traditional portfolio management. The Deep Reinforcement Learning approach shows better results than traditional approaches using a simple reward function and only being given the time series of stocks. In Finance, no training to test error generalization results come guaranteed. We can say that the modeling framework can deal with time series prediction and asset allocation, including transaction costs.
    Date: 2020–10
  4. By: Shixuan Wang (Department of Economics, University of Reading, Reading, RG6 6AA, United Kingdom); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa); Yue-Jun Zhang (College of Business Administration, University of Nebraska at Omaha, 6708 Pine Street, Omaha, NE 68182, USA)
    Abstract: In this paper, we employ a four-state hidden semi-Markov model (HSMM), which outperforms a hidden Markov model (HMM), to identify market conditions of the Dow Jones Industrial stock market over the daily period of 16th of February, 1885 to 4th of June, 2020. Our results indicate that the four hidden states represent bear-, bull-, sidewalk-, and crash-markets, which in turn appropriately captures the various major historical events during the period of study. Our results have implications for investors and policymakers.
    Keywords: Dow Jones Industrial Average, Stock Returns, Hidden (semi-)Markov Models
    JEL: C22 G10
    Date: 2020–10
  5. By: Gomez-Gonzalez, Jose Eduardo; Hirs-Garzon, Jorge; Uribe, Jorge M.
    Abstract: We estimate the effects of financial, macroeconomic and policy uncertainty from the United States on the dynamics of credit growth, stock prices, economic activity, bond yields and inflation in five of the main receptors of US foreign direct investment from 1950 to 2019: The United Kingdom, The Netherlands, Ireland, Canada and Switzerland. Our multicounty approach allows us to clearly identify the effects of the different sources of uncertainty by imposing natural contemporaneous exogenity restrictions which cannot be used in a single-country perspective, frequently undertaken by the literature. It also considers international common cycle factors that have been previously identified and which are key to adequately measure the dynamics of the effects of uncertainty shocks on financial and real markets, on a global basis. We use an international FAVAR model to carry out our estimations. This approach permits handling a large data set consisting of variables for more than 45 countries at once. Our results point out to financial uncertainty as the main driver (even more than real uncertainty or the US interest rate) of global economic cycles. We show that increases of US financial uncertainty deteriorate economic activity on a global scale, especially by reducing credit and stock prices, and therefore funding opportunities for firms and households (heterogeneously on a country level basis). Our results emphasize the importance of financial markets, and especially financial uncertainty in the United States, as the main origin of global economic fluctuations, which can be said to describe the recent history of the global economy. They also cast doubts on the ability of uncertainty indicators based on the counting of key words in the media as a barometer of traditional economic uncertainty, known to be theoretically associated to negative outcomes in terms of activity and prices. In this sense, uncertainty indicators based on the estimation and aggregation of forecast errors seem more appropriate, hence producing results in line with the understanding of uncertainty as a negative phenomenon on a macro level, especially for investment prospects.
    Keywords: macroeconomic uncertainty; financial uncertainty; credit markets; funding; global business cycles
    JEL: D80 E44 F21 F44 G15
    Date: 2020–10
  6. By: Elie Bouri (Adnan Kassar School of Business, Lebanese American University, Lebanon); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa); Anandamayee Majumdar (Department of Physical Sciences, School of Engineering, Technology & Sciences, Independent University, Bangladesh, Dhaka 1229, Bangladesh); Sowmya Subramaniam (Indian Institute of Management Lucknow, Prabandh Nagar off Sitapur Road, Lucknow, Uttar Pradesh 226013, India)
    Abstract: In this paper, we analyse the forecasting ability of a time-varying metric of daily risk aversion for the entire term structure of interest rates of Treasury securities of the United States (US) as reflected by the three latent factors, level, slope and curvature. Using daily data covering the out-of-sample period 22nd June, 1988 to 3rd September, 2020 (given the in-sample period 30th May, 1986 to 21st June, 1988) within a quantiles-based framework, the results show statistically significant forecasting gains emanating from risk aversion for the tails of the conditional distributions of the level, slope and curvature factors at horizons of one-day, one-week, and one-month-ahead. Interestingly, a conditional mean-based model fails to detect any evidence of out-of-sample predictability. Our findings have important implications for academics, bond investors, and policymakers in their quest to better understand the evolution of future movement in US Treasury securities.
    Keywords: Yield Curve Factors, Risk Aversion, Out-of-Sample Forecasts
    JEL: C22 C53 E43 G12 G17
    Date: 2020–10
  7. By: Andrew Y. Chen; Markus F. Ibert; Francisco Vazquez-Grande
    Abstract: Between March and September 2020, broad equity price indexes around the world experienced a historic rally. Although this rally followed a significant decline in stock prices, it appears difficult to explain due to continuing concerns about the global pandemic and national economies running far below their potentials.
    Date: 2020–10–14
  8. By: Ricardo Schefer
    Abstract: Expectations of risky bond payments are unobservable and recovery rates for sovereigns are hard to estimate because they have no contractual claims to defined assets and samples of defaults are limited. A geometric version of credit spread is used to derive expected payments, dependent on idiosyncratic risk and unrelated to interest rates. The expectations are used to define a measure of price sensitivity to credit risk perceptions, or credit duration, improving the ambiguity of modified yield duration.
    Keywords: bond, sovereign, spread, expected, risk neutral, default, duration, yield
    JEL: D84 F34 G12 H63
    Date: 2020–10
  9. By: Itamar Drechsler; Alan Moreira; Alexi Savov
    Abstract: Liquidity provision is a bet against private information: if private information turns out to be higher than expected, liquidity providers lose. Since information generates volatility, and volatility co-moves across assets, liquidity providers have a negative exposure to aggregate volatility shocks. As aggregate volatility shocks carry a very large premium in option markets, this negative exposure can explain why liquidity provision earns high average returns. We show this by incorporating uncertainty about the amount of private information into an otherwise standard model. We test the model in the cross section of short-term reversals, which mimic the portfolios of liquidity providers. As predicted by the model, reversals have large negative betas to aggregate volatility shocks. These betas explain their average returns with the same risk price as in option markets, and their predictability by VIX in the time series. Volatility risk thus explains the liquidity premium among stocks and why it increases in volatile times. Our results provide a novel view of the risks and returns to liquidity provision.
    JEL: E44 G12 G23
    Date: 2020–10
  10. By: Elior Nehemya; Yael Mathov; Asaf Shabtai; Yuval Elovici
    Abstract: In recent years, machine learning has become prevalent in numerous tasks, including algorithmic trading. Stock market traders utilize learning models to predict the market's behavior and execute an investment strategy accordingly. However, learning models have been shown to be susceptible to input manipulations called adversarial examples. Yet, the trading domain remains largely unexplored in the context of adversarial learning. This is mainly because of the rapid changes in the market which impair the attacker's ability to create a real-time attack. In this study, we present a realistic scenario in which an attacker gains control of an algorithmic trading bots by manipulating the input data stream in real-time. The attacker creates an universal perturbation that is agnostic to the target model and time of use, while also remaining imperceptible. We evaluate our attack on a real-world market data stream and target three different trading architectures. We show that our perturbation can fool the model at future unseen data points, in both white-box and black-box settings. We believe these findings should serve as an alert to the finance community about the threats in this area and prompt further research on the risks associated with using automated learning models in the finance domain.
    Date: 2020–10
  11. By: Isao Yagi; Mahiro Hoshino; Takanobu Mizuta
    Abstract: Recently, most stock exchanges in the U.S. employ maker-taker fees, in which an exchange pays rebates to traders placing orders in the order book and charges fees to traders taking orders from the order book. Maker-taker fees encourage traders to place many orders that provide market liquidity to the exchange. However, it is not clear how maker-taker fees affect the total cost of a taking order, including all the charged fees and the market impact. In this study, we investigated the effect of maker-taker fees on the total cost of a taking order with our artificial market model, which is an agent-based model for financial markets. We found that maker-taker fees encourage market efficiency but increase the total costs of taking orders.
    Date: 2020–10

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