nep-fmk New Economics Papers
on Financial Markets
Issue of 2022‒01‒24
eight papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. COVID-19 Forecasts via Stock Market Indicators By Yi Liang; James Unwin
  2. Repo over the Financial Crisis By Adam Copeland; Antoine Martin
  3. Stock Movement Prediction Based on Bi-typed and Hybrid-relational Market Knowledge Graph via Dual Attention Networks By Yu Zhao; Huaming Du; Ying Liu; Shaopeng Wei; Xingyan Chen; Huali Feng; Qinghong Shuai; Qing Li; Fuzhen Zhuang; Gang Kou
  4. Market efficiency in the age of big data By Martin, Ian W.R.; Nagel, Stefan
  5. Stationarity analysis of the stock market data and its application to mechanical trading By Kazuki Kanehira; Norikazu Todoroki
  6. What Drives Variation in Investor Portfolios? Evidence from Retirement Plans By Mark L. Egan; Alexander MacKay; Hanbin Yang
  7. Neural Networks for Delta Hedging By Guijin Son; Joocheol Kim
  8. Market making by an FX dealer: tiers, pricing ladders and hedging rates for optimal risk control By Alexander Barzykin; Philippe Bergault; Olivier Gu\'eant

  1. By: Yi Liang; James Unwin
    Abstract: Reliable short term forecasting can provide potentially lifesaving insights into logistical planning, and in particular, into the optimal allocation of resources such as hospital staff and equipment. By reinterpreting COVID-19 daily cases in terms of candlesticks, we are able to apply some of the most popular stock market technical indicators to obtain predictive power over the course of the pandemics. By providing a quantitative assessment of MACD, RSI, and candlestick analyses, we show their statistical significance in making predictions for both stock market data and WHO COVID-19 data. In particular, we show the utility of this novel approach by considering the identification of the beginnings of subsequent waves of the pandemic. Finally, our new methods are used to assess whether current health policies are impacting the growth in new COVID-19 cases.
    Date: 2021–12
  2. By: Adam Copeland; Antoine Martin
    Abstract: This paper uses new data to provide a comprehensive view of repo activity during the 2007-09 financial crisis for the first time. We show that activity declined much more in the bilateral segment of the market than in the tri-party segment. Surprisingly, we find that a large share of the decline in activity is driven by repos backed by Treasury securities. Further, a disproportionate share of the decline in repo activity is connected to securities dealer’s market-making activity in Treasury securities. In particular, the evidence suggests that at least part of the decline is not driven by clients pulling away from securities dealers because of counterparty credit concerns.
    Keywords: repo; financial crisis; money markets
    JEL: G01 G23 E42
    Date: 2021–12–01
  3. By: Yu Zhao; Huaming Du; Ying Liu; Shaopeng Wei; Xingyan Chen; Huali Feng; Qinghong Shuai; Qing Li; Fuzhen Zhuang; Gang Kou
    Abstract: Stock Movement Prediction (SMP) aims at predicting listed companies' stock future price trend, which is a challenging task due to the volatile nature of financial markets. Recent financial studies show that the momentum spillover effect plays a significant role in stock fluctuation. However, previous studies typically only learn the simple connection information among related companies, which inevitably fail to model complex relations of listed companies in the real financial market. To address this issue, we first construct a more comprehensive Market Knowledge Graph (MKG) which contains bi-typed entities including listed companies and their associated executives, and hybrid-relations including the explicit relations and implicit relations. Afterward, we propose DanSmp, a novel Dual Attention Networks to learn the momentum spillover signals based upon the constructed MKG for stock prediction. The empirical experiments on our constructed datasets against nine SOTA baselines demonstrate that the proposed DanSmp is capable of improving stock prediction with the constructed MKG.
    Date: 2022–01
  4. By: Martin, Ian W.R.; Nagel, Stefan
    Abstract: Modern investors face a high-dimensional prediction problem: thousands of observable variables are potentially relevant for forecasting. We reassess the conventional wisdom on market efficiency in light of this fact. In our equilibrium model, N assets have cash flows that are linear in J characteristics, with unknown coefficients. Risk-neutral Bayesian investors learn these coefficients and determine market prices. If J and N are comparable in size, returns are cross-sectionally predictable ex post. In-sample tests of market efficiency reject the no-predictability null with high probability, even though investors use information optimally in real time. In contrast, out-of-sample tests retain their economic meaning.
    Keywords: Bayesian learning; high-dimensional prediction problems; return predictability; out-of-sample tests; Starting Grant 639744; Center for Research in Security Prices
    JEL: G14 G12 C11
    Date: 2021–11–27
  5. By: Kazuki Kanehira; Norikazu Todoroki
    Abstract: This study proposes a scheme for stationarity analysis of stock price fluctuations based on KM$_2$O-Langevin theory. Using this scheme, we classify the time-series data of stock price fluctuations into three periods: stationary, non-stationary, and intermediate. We then suggest an example of a low-risk stock trading strategy to demonstrate the usefulness of our scheme by using actual stock index data. Our strategy uses a trend-based indicator, moving averages, for stationary periods and an oscillator-based indicator, psychological lines, for non-stationary periods to make trading decisions. Finally, we confirm that our strategy is a safe trading strategy with small maximum drawdown by back testing on the Nikkei Stock Average. Our study, the first to apply the stationarity analysis of KM$_2$O-Langevin theory to actual mechanical trading, opens up new avenues for stock price prediction.
    Date: 2021–12
  6. By: Mark L. Egan; Alexander MacKay; Hanbin Yang
    Abstract: We study empirical patterns in investment behavior using a comprehensive data set of defined contribution plans. Using plan-level portfolio allocation data for the near universe of 401(k) plans over the period 2009-2019, we document substantial differences in investment behavior across plans. Plans with wealthier and more educated participants tend to have higher equity exposure while plans with more retirees and minorities tend to have lower equity exposure. These patterns cannot be explained by differences in 401(k) menus or participation costs. To help interpret these facts, we use a revealed preference approach to estimate investors' expectations of stock market returns and risk aversion, where we allow investors to have heterogeneous risk aversion and subjective and potentially biased beliefs. We find that there is substantial variation in both beliefs and risk aversion across investors and over time, and that both sources of variation help explain investors' portfolio decisions. We also provide new evidence to understand how investors form beliefs. We find that investors extrapolate beliefs from past fund returns even when they initially allocate portfolios in new plans. We also find that investors extrapolate beliefs about the market from the past performance of their employer, which suggests that investor experience helps shape beliefs.
    JEL: G0 G11 G12 G40 G5 G51 J32
    Date: 2021–12
  7. By: Guijin Son; Joocheol Kim
    Abstract: The Black-Scholes model, defined under the assumption of a perfect financial market, theoretically creates a flawless hedging strategy allowing the trader to evade risks in a portfolio of options. However, the concept of a "perfect financial market," which requires zero transaction and continuous trading, is challenging to meet in the real world. Despite such widely known limitations, academics have failed to develop alternative models successful enough to be long-established. In this paper, we explore the landscape of Deep Neural Networks(DNN) based hedging systems by testing the hedging capacity of the following neural architectures: Recurrent Neural Networks, Temporal Convolutional Networks, Attention Networks, and Span Multi-Layer Perceptron Networks. In addition, we attempt to achieve even more promising results by combining traditional derivative hedging models with DNN based approaches. Lastly, we construct \textbf{NNHedge}, a deep learning framework that provides seamless pipelines for model development and assessment for the experiments.
    Date: 2021–12
  8. By: Alexander Barzykin; Philippe Bergault; Olivier Gu\'eant
    Abstract: Dealers make money by providing liquidity to clients but face flow uncertainty and thus price risk. They can efficiently skew their prices and wait for clients to mitigate risk (internalization), or trade with other dealers in the open market to hedge their position and reduce their inventory (externalization). Of course, the better control associated with externalization comes with transaction costs and market impact. The internalization vs. externalization dilemma has been a topic of recent active discussion within the foreign exchange (FX) community. This paper offers an optimal control framework for market making tackling both pricing and hedging, thus answering a question well known to dealers: `to hedge, or not to hedge?'
    Date: 2021–12

This nep-fmk issue is ©2022 by Kwang Soo Cheong. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
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.