nep-fmk New Economics Papers
on Financial Markets
Issue of 2019‒03‒25
thirteen papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. CAPM: A Tale of Two Versions By Siddiqi, Hammad
  2. Drawbacks in the 3-factor approach of Fama and French By David E. Allen; Michael McAleer
  3. STOCK PRICE ANCHORING By Mustafa Disli; Koen Inghelbrecht; Koen Schoors; Hannes Stieperaere
  4. Risk and Return models for Equity Markets and Implied Equity Risk Premium By Enzo Busseti
  5. The Total Risk Premium Puzzle By Òscar Jordà; Moritz Schularick; Alan M. Taylor
  6. Optimal FX Hedge Tenor with Liquidity Risk By Rongju Zhang; Mark Aarons; Gregoire Loeper
  7. Multimodal Deep Learning for Finance: Integrating and Forecasting International Stock Markets By Sang Il Lee; Seong Joon Yoo
  8. Bursting the Bitcoin Bubble: Assessing the Fundamental Value and Social Costs of Bitcoin By Podhorsky, Andrea
  9. Trading Costs and Informational Efficiency By Eduardo Dávila; Cecilia Parlatore
  10. Trade Policy Uncertainty and Stock Returns By Marcelo Bianconi; Federico Esposito; Marco Sammon
  11. Market-Making Costs and Liquidity: Evidence from CDS Markets By Mark Paddrik; Stathis Tompaidis
  12. Inferring Term Rates from SOFR Futures Prices By Erik Heitfield; Yang-Ho Park
  13. Designing an Optimal Portfolio for Iran's Stock Market with Genetic Algorithm using Neural Network Prediction of Risk and Return Stocks By Masoud Fekri; Babak Barazandeh

  1. By: Siddiqi, Hammad
    Abstract: Categorization is the mental operation by which brain classifies objects and events. We do not experience the world as a series of unique events. Rather, we make sense of our experiences within a framework of categories that represent prior knowledge. Given that categorization is the core of cognition, we argue that the traditional view that each firm is viewed in isolation needs to be altered. Instead, like every other object they ever come across, investors view each firm within a framework of categories that represent prior knowledge. This involves sorting a firm into a category based on a subset of firm-attributes. Such categorization-relevant attributes are refined whereas other firm-attributes are confounded with the category-exemplar. Two versions of CAPM arise as a result. In the first version, the relationship between average excess return and stock beta is flat (possibly negative). Value effect and size premium (controlling for quality) arise in this version. In the second version, the relationship is strongly positive. The two-version CAPM accounts for several recent empirical findings including fundamentally different intraday vs overnight behavior, as well as behavior on macroeconomic announcement days. The tug-of-war dynamics of the two versions also suggest that momentum is expected to be an overnight phenomenon, which is consistent with empirical findings. We argue that, perhaps, our best shot at observing classical CAPM in its full glory is a laboratory experiment with subjects who have difficulty categorizing (such as in autism spectrum disorders).
    Keywords: CAPM, Categorization, Value Effect, Betting-Against-Beta, Size Effect, Momentum Effect
    JEL: G00 G02 G1 G12 G3 G30
    Date: 2019–03–01
  2. By: David E. Allen (School of Mathematics and Statistics, University of Sydney, Department of Finance, Asia University, Taiwan, and School of Business and Law, Edith Cowan University, Australia.); Michael McAleer (Department of Quantitative Finance National Tsing Hua University, Taiwan and Econometric Institute Erasmus School of Economics Erasmus University Rotterdam, The Netherlands and Department of Quantitative Economics Complutense University of Madrid, Spain And Institute of Advanced Sciences Yokohama National University, Japan.)
    Abstract: This paper features a statistical analysis of the monthly three factor Fama/French return series. We apply rolling OLS regressions to explore the relationship between the 3 factors, using monthly and weekly data from July 1926 to June 2018, that are freely available on French's website. The results suggest there are significant and time-varying relationships between the factors. This is confirmed by non-parametric tests. We then switch to a sub-sample from July 1990 to July 2018, also taken from French's website. The three series and their interrelationships are analysed using two stage least squares and the Hausman test to check for issues related to endogeneity, the Sargan overidentification test and the Cragg-Donald weak instrument test. The relationship between factors is also examined using OLS, incorporating Ramsey's RESET tests of functional form misspecification, plus Naradaya-Watson kernel regression techniques. The empirical results suggest that the factors, when combined in OLS regression analysis, as suggested by Fama and French (2018), are likely to suffer from endogeneity. OLS regression analysis and the application of Ramsey's RESET tests suggest a non-linear relationship exists between the three series, in which cubed terms are significant. This non-linearity is also confirmed by the kernel regression analysis. We use two instruments to estimate the market betas, and then use the factor estimates in a second set of panel data tests using a small sample of monthly returns for US firms that are drawn from the online data source “tingo”. These issues are analysed using methods suggested by Petersen (2009) to permit clustering in the panels by date and firm. The empirical results suggest that using an instrument to capture endogeneity reducesthe standard error of market beta in subsequent crosssectional tests, but thatclustering effects, as suggested by Petersen (2009), will also impact on the estimated standard errors. The empirical results suggest that using these factorsin linear regression analysis, such as suggested by Fama and French (2018), as a method of screening factor relevance, is problematic in that the estimated standard errors are highly sensitive to the correct model specification.
    Keywords: Fama-French Factors, Correct specification, Ramsey's RESET, Hausman tests, Endogeneity, Consistent standard errors.
    JEL: C13 C14 G12
  3. By: Mustafa Disli; Koen Inghelbrecht; Koen Schoors; Hannes Stieperaere (-)
    Abstract: We provide evidence on a new anomaly in the stock market. We show that stock prices are very robustly correlated to firm value in a cross-sectional framework. We interpret this result as evidence that investors' valuations are biased by a specific version of the availability heuristic, by which investors wrongly interpret the easily available infor- mation about the stock price as a piece of relevant cross-sectional information about true firm value. In this way firm value is \anchored" to the stock price, confirming the existence of anchoring e ects in financial markets beyond the boundaries of the experimental lab. Interestingly, firms with a high nominal share price at the end of the year, tend to have lower returns in the subsequent year. After controlling for common risk factors, this underperformance amounts to 1.77 basis points per day, or 4.56% per annum.
    Keywords: Anchoring e ect, heuristics, anomaly, firm value, stock prices
    JEL: G02 G11 G14
    Date: 2019–03
  4. By: Enzo Busseti
    Abstract: Equity risk premium is a central component of every risk and return model in finance and a key input to estimate costs of equity and capital in both corporate finance and valuation. An article by Damodaran examines three broad approaches for estimating the equity risk premium. The first is survey based, it consists in asking common investors or big players like pension fund managers what they require as a premium to invest in equity. The second is to look at the premia earned historically by investing in stocks, as opposed to risk-free investments. The third method tries to extrapolate a market-consensus on equity risk premium (Implied Equity Risk Premium) by analysing equity prices on the market today. After having introduced some basic concepts and models, I'll briefly explain the pluses and minuses of the first two methods, and analyse more deeply the third. In the end I'll show the results of my estimation of ERP on real data, using variants of the Implied ERP (third) method.
    Date: 2019–03
  5. By: Òscar Jordà; Moritz Schularick; Alan M. Taylor
    Abstract: The risk premium puzzle is worse than you think. Using a new database for the U.S. and 15 other advanced economies from 1870 to the present that includes housing as well as equity returns (to capture the full risky capital portfolio of the representative agent), standard calculations using returns to total wealth and consumption show that: housing returns in the long run are comparable to those of equities, and yet housing returns have lower volatility and lower covariance with consumption growth than equities. The same applies to a weighted total-wealth portfolio, and over a range of horizons. As a result, the implied risk aversion parameters for housing wealth and total wealth are even larger than those for equities, often by a factor of 2 or more. We find that more exotic models cannot resolve these even bigger puzzles, and we see little role for limited participation, idiosyncratic housing risk, transaction costs, or liquidity premiums.
    JEL: E44 G12 G15 N20
    Date: 2019–03
  6. By: Rongju Zhang; Mark Aarons; Gregoire Loeper
    Abstract: We develop an optimal currency hedging strategy for fund managers who own foreign assets to choose the hedge tenors that maximize their FX carry returns within a liquidity risk constraint. The strategy assumes that the offshore assets are fully hedged with FX forwards. The chosen liquidity risk metric is Cash Flow at Risk (CFaR). The strategy involves time-dispersing the total nominal hedge value into future time buckets to maximize (minimize) the expected FX carry benefit (cost), given the constraint that the CFaRs in all the future time buckets do not breach a predetermined liquidity budget. We demonstrate the methodology via an illustrative example where shorter-dated forwards are assumed to deliver higher carry trade returns (motivated by the historical experience where AUD is the domestic currency and USD is the foreign currency). We also introduce a tenor-ranking method which is useful when this assumption fails. We show by Monte Carlo simulation and by backtesting that our hedging strategy successfully operates within the liquidity budget. We provide practical insights on when and why fund managers should choose short-dated or long-dated tenors.
    Date: 2019–03
  7. By: Sang Il Lee; Seong Joon Yoo
    Abstract: Stock prices are influenced by numerous factors. We present a method to combine these factors and we validate the method by taking the international stock market as a case study. In today's increasingly international economy, return and volatility spillover effects across international equity markets are major macroeconomic drivers of stock dynamics. Thus, foreign market information is one of the most important factors in forecasting domestic stock prices. However, the cross-correlation between domestic and foreign markets is so complex that it would be extremely difficult to express it explicitly with a dynamical equation. In this study, we develop stock return prediction models that can jointly consider international markets, using multimodal deep learning. Our contributions are three-fold: (1) we visualize the transfer information between South Korea and US stock markets using scatter plots; (2) we incorporate the information into stock prediction using multimodal deep learning; (3) we conclusively show that both early and late fusion models achieve a significant performance boost in comparison with single modality models. Our study indicates that considering international stock markets jointly can improve prediction accuracy, and deep neural networks are very effective for such tasks.
    Date: 2019–03
  8. By: Podhorsky, Andrea (Asian Development Bank Institute)
    Abstract: This paper develops a microeconomic model of bitcoin production to analyze the economic effects of the Bitcoin protocol. I view the bitcoin as a tradable commodity that is produced by miners and whose supply is managed by the protocol. The findings show that bitcoin’s volatile price path and inefficiency are related, and that both are a consequence of the protocol’s system of supply management. I characterize the fundamental value of a bitcoin and demonstrate that the return on bitcoin appreciates proportionally to the rate of increase in the level of difficulty. In the model, where the price of a bitcoin is based on marginal production costs, successive positive demand shocks result in a rapidly increasing price path that may be mistaken for a bubble. The generalized supremum augmented Dickey-Fuller (GSADF) test is used to demonstrate that the model is able to account for the explosive behavior in the bitcoin price path, providing strong evidence that bitcoin is not a bubble. I also show that the difficulty adjustment mechanism results in social welfare losses from 17 March 2014 to 13 January 2019 of $323.8 million, which is about 9.3% of the miners’ total electricity costs during this time period.
    Keywords: bitcoin; digital coins; Bitcoin protocol; cryptocurrency; bitcoin bubble
    JEL: F30 G00 G11
    Date: 2019–03–19
  9. By: Eduardo Dávila; Cecilia Parlatore
    Abstract: We study the effect of trading costs on information aggregation and acquisition in financial markets. For a given precision of investors' private information, an irrelevance result emerges when investors are ex-ante identical: price informativeness is independent of the level of trading costs. When investors are ex-ante heterogeneous, anything goes, and a change in trading costs can increase or decrease price informativeness, depending on the source of heterogeneity. Our results are valid under quadratic, linear, and fixed costs. Through a reduction in information acquisition, trading costs reduce price informativeness. We discuss how our results inform the policy debate on financial transaction taxes/Tobin taxes.
    JEL: D82 D83 G14
    Date: 2019–03
  10. By: Marcelo Bianconi; Federico Esposito; Marco Sammon
    Abstract: This paper documents new stylized facts on the effects of trade policy uncertainty on stock returns. We exploit quasi-exogenous variation in exposure to policy uncertainty arising from annual votes by US Congress to revoke China's MFN tariff rates between 1990 and 2000. Before the uncertainty was resolved by granting China permanent MFN rates, US manufacturing industries highly exposed to trade policy uncertainty had stock returns 10.4% higher per year than less exposed sectors. We argue that this difference in average returns is a risk premium for exposure to trade policy uncertainty. Indirect exposure to trade policy uncertainty through Input-Output linkages also commands a substantial risk premium.
    Date: 2019
  11. By: Mark Paddrik (Office of Financial Research); Stathis Tompaidis (University of Texas at Austin)
    Abstract: In over-the-counter markets, dealers facilitate trading by becoming market makers. The costs dealers face, including the cost of holding inventory on balance sheet, and the ease, or difficulty, of reducing their positions, determine the degree of liquidity they provide. We provide a stylized model to examine the implications of these costs on dealer behavior and market liquidity. We use the model to guide an empirical study of the single-name credit default swap (CDS) market between 2010-2016. We find that transaction prices between dealers and clients have progressively become more dependent on the inventories of individual dealers rather than on the aggregate inventory across all dealers. We also find that the volume between clients and dealers decreases across all clients, with larger declines for clients that are depository institutions. At the same time, the volume of interdealer trades decreases, dealer inventories decline, and dealers with large inventories are more likely to trade with clients. Our results are consistent with the view that regulatory reforms implemented following the 2007-09 financial crisis increased the cost of holding inventory for dealers, and the cost of interdealer trading.
    Keywords: credit default swaps, liquidity, market making, transaction costs
    Date: 2019–03–12
  12. By: Erik Heitfield; Yang-Ho Park
    Abstract: The Alternative Reference Rate Committee, a group of private-sector market participants convened by the Federal Reserve, has recommended that markets transition to the use of the Secured Overnight Financing Rate (SOFR) in financial contracts that currently reference US dollar LIBOR. This paper examines the feasibility of using SOFR futures prices to construct forward-looking term reference rates that are conceptually similar to the term LIBOR rates commonly used in loan contracts. We show that futures-implied term SOFR rates have closely tracked federal funds OIS rates over the eight months since SOFR futures began trading. To examine the performance of our approach over a longer time horizon, we compare term rates derived from federal funds futures with observed overnight rates and OIS rates from 2000 to the present. Consistent with prior research, we find that futures-implied term rates accurately predict realized compounded overnight rates during most periods.
    Keywords: Interest rates ; Futures ; Reference rates ; Financial contracts ; LIBOR ; SOFR
    JEL: G12 G18
    Date: 2019–03–05
  13. By: Masoud Fekri; Babak Barazandeh
    Abstract: Optimal capital allocation between different assets is an important financial problem, which is generally framed as the portfolio optimization problem. General models include the single-period and multi-period cases. The traditional Mean-Variance model introduced by Harry Markowitz has been the basis of many models used to solve the portfolio optimization problem. The overall goal is to achieve the highest return and lowest risk in portfolio optimization problems. In this paper, we will present an optimal portfolio based the Markowitz Mean-Variance-Skewness with weight constraints model for short-term investment opportunities in Iran's stock market. We will use a neural network based predictor to predict the stock returns and measure the risk of stocks based on the prediction errors in the neural network. We will perform a series of experiments on our portfolio optimization model with the real data from Iran's stock market indices including Bank, Insurance, Investment, Petroleum Products and Chemicals indices. Finally, 8 different portfolios with low, medium and high risks for different type of investors (risk-averse or risk taker) using genetic algorithm will be designed and analyzed.
    Date: 2019–02

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