nep-fmk New Economics Papers
on Financial Markets
Issue of 2019‒12‒16
nine papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Public versus Private Equity By Stulz, Rene M.
  2. Forecasting Realized Volatility: The role of implied volatility, leverage effect, overnight returns and volatility of realized volatility By Dimos Kambouroudis; David McMillan; Katerina Tsakou
  3. An Integrated Early Warning System for Stock Market Turbulence By Peiwan Wang; Lu Zong; Ye Ma
  4. Financial ratios and stock returns reappraised through a topological data analysis lens By Pawel Dlotko; Wanling Qiu; Simon Rudkin
  5. U-CNNpred: A Universal CNN-based Predictor for Stock Markets By Ehsan Hoseinzade; Saman Haratizadeh; Arash Khoeini
  6. Bundling, Belief Dispersion, and Mispricing in Financial Markets By Milo Bianchi; Philippe Jehiel
  7. Interbank network characteristics, monetary policy "News" and sensitivity of bank stock returns By Aref Ardekani; Isabelle Distinguin; Amine Tarazi
  8. Dynamic Portfolio Management with Reinforcement Learning By Junhao Wang; Yinheng Li; Yijie Cao
  9. Does Stock Market Listing Impact Investment in Japan? By Joseph J. French; Ryosuke Fujitani; Yukihiro Yasuda

  1. By: Stulz, Rene M. (Ohio State University (OSU) - Department of Finance; National Bureau of Economic Research (NBER); European Corporate Governance Institute (ECGI))
    Abstract: The last twenty years or so have seen a sharp decline in public equity. I present a framework that explains the forces that cause the listing propensity of firms to change over time. This framework highlights the benefits and costs of a public listing compared to the benefits and costs of financing with private equity. With this framework, the decline in public equity is explained by the increased supply of funds for private equity and changes in the nature of firms. The increase in the importance of intangible assets makes it costlier for young firms to be public when the alternative is funding through private equity from investors who have specialized knowledge that enables them to better understand the business model of young firms and contribute to the development of that business model in contrast to passive public equity investors.
    JEL: G12 G17 G18 G32 K22
    Date: 2019–11
  2. By: Dimos Kambouroudis (Department of Accounting and Finance, University of Stirling); David McMillan (Department of Accounting and Finance, University of Stirling); Katerina Tsakou (School of Management, Swansea University)
    Abstract: We examine the role of implied volatility, leverage effect, overnight returns and volatility of realized volatility in forecasting realized volatility by extending the heterogeneous autoregressive (HAR) model to include these additional variables. We find that implied volatility is important in forecasting future realized volatility. In most cases a model that accounts for implied volatility provides a significantly better forecast than more sophisticated models that account for other features of volatility, but exclude the information backed out from option prices. This result is consistent over time. We also assess whether leverage effect, overnight returns and volatility of realized volatility carry any incremental information beyond that captured by implied volatility and past realized volatility. We find that while overnight returns and leverage e˙ect are important for some markets, the volatility of realized volatility is of limited value for most stock markets.
    Keywords: HAR model, realized volatility, implied volatility, implied volatility effects, leverage effect, overnight returns, GARCH
    Date: 2019–12–12
  3. By: Peiwan Wang; Lu Zong; Ye Ma
    Abstract: This study constructs an integrated early warning system (EWS) that identifies and predicts stock market turbulence. Based on switching ARCH (SWARCH) filtering probabilities of the high volatility regime, the proposed EWS first classifies stock market crises according to an indicator function with thresholds dynamically selected by the two-peak method. A hybrid algorithm is then developed in the framework of a long short-term memory (LSTM) network to make daily predictions that alert turmoils. In the empirical evaluation based on ten-year Chinese stock data, the proposed EWS yields satisfying results with the test-set accuracy of $96.6\%$ and on average $2.4$ days of the forewarned period. The model's stability and practical value in real-time decision-making are also proven by the cross-validation and back-testing.
    Date: 2019–11
  4. By: Pawel Dlotko; Wanling Qiu; Simon Rudkin
    Abstract: Firm financials are well established as return predictors, being the inspiration for a large set of anomalies in the asset pricing literature. Employing topological data analysis we revisit the question of association between seven of the most commonly studied financial ratios and stock returns. Specifically the TDA Ball Mapper algorithm is applied to visualise the point cloud of financial ratios as an abstract two-dimensional graph readily allowing for identification of interdependencies between factors. These relationships are seldom monotonic, opportunities for investors to profitably exploit this knowledge provided by TDA abound. Clear potential offered by the tools of TDA to shed new light on asset pricing models is demonstrated. Scope for benefit is limited only by the availability of information to the analyst.
    Date: 2019–11
  5. By: Ehsan Hoseinzade; Saman Haratizadeh; Arash Khoeini
    Abstract: The performance of financial market prediction systems depends heavily on the quality of features it is using. While researchers have used various techniques for enhancing the stock specific features, less attention has been paid to extracting features that represent general mechanism of financial markets. In this paper, we investigate the importance of extracting such general features in stock market prediction domain and show how it can improve the performance of financial market prediction. We present a framework called U-CNNpred, that uses a CNN-based structure. A base model is trained in a specially designed layer-wise training procedure over a pool of historical data from many financial markets, in order to extract the common patterns from different markets. Our experiments, in which we have used hundreds of stocks in S\&P 500 as well as 14 famous indices around the world, show that this model can outperform baseline algorithms when predicting the directional movement of the markets for which it has been trained for. We also show that the base model can be fine-tuned for predicting new markets and achieve a better performance compared to the state of the art baseline algorithms that focus on constructing market-specific models from scratch.
    Date: 2019–11
  6. By: Milo Bianchi (TSE - Toulouse School of Economics - UT1 - Université Toulouse 1 Capitole - CNRS - Centre National de la Recherche Scientifique - INRA - Institut National de la Recherche Agronomique - EHESS - École des hautes études en sciences sociales); Philippe Jehiel (PJSE - Paris Jourdan Sciences Economiques - UP1 - Université Panthéon-Sorbonne - ENS Paris - École normale supérieure - Paris - INRA - Institut National de la Recherche Agronomique - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics, UCL - University College of London [London])
    Abstract: Bundling assets of heterogeneous quality results in dispersed valuations when these are based on investor-specific samples from the pool. A monop olistic bank has the incentive to create heterogeneous bundles only when investors have enough money as in that case prices are driven by more opti- mistic valuations. When the number of banks is sufficiently large, oligopolistic banks choose extremely heterogeneous bundles even when investors have little money and even if this turns out to be collectively detrimental to the banks, which we refer to as a Bundler.s Dilemma.
    Keywords: complexnancial products,bounded rationality,disagreement,market efficiency
    Date: 2019–07
  7. By: Aref Ardekani (LAPE - Laboratoire d'Analyse et de Prospective Economique - IR SHS UNILIM - Institut Sciences de l'Homme et de la Société - UNILIM - Université de Limoges); Isabelle Distinguin (LAPE - Laboratoire d'Analyse et de Prospective Economique - IR SHS UNILIM - Institut Sciences de l'Homme et de la Société - UNILIM - Université de Limoges); Amine Tarazi (LAPE - Laboratoire d'Analyse et de Prospective Economique - IR SHS UNILIM - Institut Sciences de l'Homme et de la Société - UNILIM - Université de Limoges)
    Abstract: This paper investigates whether interbank network topology influences the impact of monetary policy announcements on bank cumulative abnormal returns (CAR's). Although recent studies have emphasized the channels of non-conventional monetary policy actions and the sensitivity of bank stock prices to "News", how such reaction could be influenced by the shape of bank networks remains an open issue. We look at how banks' interconnectedness within interbank loan and deposit networks affects investors' expectations of future bank performance in response to monetary policy "News". Our sample consists of commercial, investment, real estate and mortgage banks in 10 Euro-zone countries. Our results show that the stock prices of banks with stronger local network positions are less sensitive to monetary policy announcements while those of banks with stronger system-wide positions are more sensitive to them.
    Keywords: Interbank network topology,Monetary policy,bank's stock reaction,event study
    Date: 2019–11–28
  8. By: Junhao Wang; Yinheng Li; Yijie Cao
    Abstract: Dynamic Portfolio Management is a domain that concerns the continuous redistribution of assets within a portfolio to maximize the total return in a given period of time. With the recent advancement in machine learning and artificial intelligence, many efforts have been put in designing and discovering efficient algorithmic ways to manage the portfolio. This paper presents two different reinforcement learning agents, policy gradient actor-critic and evolution strategy. The performance of the two agents is compared during backtesting. We also discuss the problem set up from state space design, to state value function approximator and policy control design. We include the short position to give the agent more flexibility during assets redistribution and a constant trading cost of 0.25%. The agent is able to achieve 5% return in 10 days daily trading despite 0.25% trading cost.
    Date: 2019–11
  9. By: Joseph J. French; Ryosuke Fujitani; Yukihiro Yasuda
    Abstract: We provide the first large sample comparison of investment by Japanese listed and unlisted public firms. We show that listed firms invest more and have greater sensitivity to investment opportunities than comparable unlisted companies. Our findings suggest that the role of listing in alleviating financial constraints is more important than potential underinvestment due to myopic behavior. However, the positive relationship between listing and investment is primarily driven by standalone firms. Further analysis confirms that as the number of subsidiaries in a business group increases the positive impact of listing on investment declines. Additionally, when a firm faces financial constraints listing more positively impacts investment. We also document a positive association between stock liquidity and investment for listed firms. Taken together, our results suggest that stock markets play an important role in easing financial constraints and preventing managerial shirking both of which increase investment. Finally, we show that higher levels of owner-ship by financial institutions, board members, and foreign investors increases corporate investment.
    JEL: F3 G20 G31 G39
    Date: 2019–11

This nep-fmk issue is ©2019 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.
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