nep-fmk New Economics Papers
on Financial Markets
Issue of 2020‒01‒06
seven papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Celebrating Three Decades of Worldwide Stock Market Manipulation By Bruce Knuteson
  2. Risk on-Risk off: A regime switching model for active portfolio management By José P. Dapena; Juan A. Serur; Julián R. Siri
  3. Q-factors and Investment CAPM By Lu Zhang
  4. Four-factor model of Quanto CDS with jumps-at-default and stochastic recovery By Andrey Itkin; Fazlollah Soleymani
  5. Index Funds and the Future of Corporate Governance: Theory, Evidence, and Policy By Lucian A. Bebchuk; Scott Hirst
  6. A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing By Sidra Mehtab; Jaydip Sen
  7. Peer effects in stock market participation: Evidence from immigration By Anastasia Girshina; Thomas Y. Mathä; Michael Ziegelmeyer

  1. By: Bruce Knuteson
    Abstract: As the decade turns, we reflect on nearly thirty years of successful manipulation of the world's public equity markets. This reflection highlights a few of the key enabling ingredients and lessons learned along the way. A quantitative understanding of market impact and its decay, which we cover briefly, lets you move long-term market prices to your advantage at acceptable cost. Hiding your footprints turns out to be less important than moving prices in the direction most people want them to move. Widespread (if misplaced) trust of market prices -- buttressed by overestimates of the cost of manipulation and underestimates of the benefits to certain market participants -- makes price manipulation a particularly valuable and profitable tool. Of the many recent stories heralding the dawn of the present golden age of misinformation, the manipulation leading to the remarkable increase in the market capitalization of the world's publicly traded companies over the past three decades is among the best.
    Date: 2019–11
  2. By: José P. Dapena; Juan A. Serur; Julián R. Siri
    Abstract: Unlike passive management, where investors almost do not buy and sell securities, active management involves a set of trading rules that govern investment decisions regarding mainly market timing. In this paper, we take the basics of active management and the two fund separation approach, to exploit the fact that an investor can switch between the market portfolio and the risk free asset according to the perceived state of the nature. Our purpose is to evaluate if there is an active management premium by testing performance with our own non-conventional multifactor model, constructed with a Hidden Markov Model which depending on the market states signaled by the level of volatility spread. We have documented that effectively, there is present a premium for actively manage the strategies, giving evidence against the idea that “active managers” destroy capital. We then propose the volatility spread as the active management factor into the Carhart´s model used to evaluate trading strategies with respect to a benchmark portfolio.
    Keywords: Regime switching, active investment, two fund separation, excess returns, hidden markov model, VIX.
    JEL: C1 C3 N2 G11
    Date: 2019–12
  3. By: Lu Zhang
    Abstract: The q-factor model shows strong explanatory power and largely summarizes the cross section of average stock returns. In particular, the q-factor model fully subsumes the Fama-French (2018) 6-factor model in head-to-head factor spanning tests. The q-factor model is an empirical implementation of the investment CAPM. The basic philosophy is to price risky assets from the perspective of their suppliers (firms), as opposed to their buyers (investors). As a disruptive innovation, the investment CAPM has broad-ranging implications for academic finance and asset management practice.
    JEL: E13 E22 E32 E44 G12 G14 G31 M41
    Date: 2019–12
  4. By: Andrey Itkin; Fazlollah Soleymani
    Abstract: In this paper we modify the model of Itkin, Shcherbakov and Veygman, (2019) (ISV2019), proposed for pricing Quanto Credit Default Swaps (CDS) and risky bonds, in several ways. First, it is known since the Lehman Brothers bankruptcy that the recovery rate could significantly vary right before or at default, therefore, in this paper we consider it to be stochastic. Second, to reduce complexity of the model, we treat the domestic interest rate as deterministic, because, as shown in ISV2019, volatility of the domestic interest rate does not contribute much to the value of the Quanto CDS spread. Finally, to solve the corresponding systems of 4D partial differential equations we use a different flavor of the Radial Basis Function (RBF) method which is a combination of localized RBF and finite-difference methods, and is known in the literature as RBF-FD. Results of our numerical experiments presented in the paper demonstrate that the influence of volatility of the recovery rate is significant if the correlation between the recovery rate and the log-intensity of the default is non-zero. Also, the impact of the recovery mean-reversion rate on the Quanto CDS spread could be comparable with the impact due to jump-at-default in the FX rate.
    Date: 2019–12
  5. By: Lucian A. Bebchuk; Scott Hirst
    Abstract: We seek to contribute to understanding index fund stewardship by providing a comprehensive theoretical, empirical, and policy analysis of such stewardship. We put forward an agency-cost theory of the stewardship decisions that index fund managers make. Our agency-costs analysis shows that index fund managers have strong incentives to (i) underinvest in stewardship and (ii) defer excessively to the preferences and positions of corporate managers. We also undertake an empirical analysis of the full range of stewardship activities that index funds do and do not undertake. We show that the body of evidence is, on the whole, consistent with the incentive issues identified by our agency-costs framework. Finally, we explain how our analysis should reorient important ongoing debates regarding common ownership and hedge fund activism.
    JEL: G23 G34 K22
    Date: 2019–12
  6. By: Sidra Mehtab; Jaydip Sen
    Abstract: Prediction of future movement of stock prices has been a subject matter of many research work. There is a gamut of literature of technical analysis of stock prices where the objective is to identify patterns in stock price movements and derive profit from it. Improving the prediction accuracy remains the single most challenge in this area of research. We propose a hybrid approach for stock price movement prediction using machine learning, deep learning, and natural language processing. We select the NIFTY 50 index values of the National Stock Exchange of India, and collect its daily price movement over a period of three years (2015 to 2017). Based on the data of 2015 to 2017, we build various predictive models using machine learning, and then use those models to predict the closing value of NIFTY 50 for the period January 2018 till June 2019 with a prediction horizon of one week. For predicting the price movement patterns, we use a number of classification techniques, while for predicting the actual closing price of the stock, various regression models have been used. We also build a Long and Short-Term Memory - based deep learning network for predicting the closing price of the stocks and compare the prediction accuracies of the machine learning models with the LSTM model. We further augment the predictive model by integrating a sentiment analysis module on twitter data to correlate the public sentiment of stock prices with the market sentiment. This has been done using twitter sentiment and previous week closing values to predict stock price movement for the next week. We tested our proposed scheme using a cross validation method based on Self Organizing Fuzzy Neural Networks and found extremely interesting results.
    Date: 2019–12
  7. By: Anastasia Girshina; Thomas Y. Mathä; Michael Ziegelmeyer
    Abstract: This paper studies how peers’ financial behaviour affects individuals’ own investment choices. To identify the peer effect, we exploit the unique composition of the Luxembourg population and use the differences in stock market participation across various immigrant groups to study how they affect stock market participation of natives. We solve the reflection problem by instrumenting immigrants’ stock market participation with lagged participation rates in their countries of birth. We separate the peer effect from the contextual and correlated effects by controlling for neighbourhood and individual characteristics. We find that stock market participation of immigrant peers has sizeable effects on that of natives. We also provide evidence that social learning is one of the channels through which the peer effect is transmitted. However, social learning alone does not account for the entire effect and we conclude that social utility might also play an important role in peer effects transmission.
    Keywords: Peer effects, stock market participation, social utility, social learning
    JEL: D14 D83 G11 I22
    Date: 2019–12

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