Dynamic portfolio optimization with liquidity cost and market impact: a simulation-and-regression approach

We present a simulation-and-regression method for solving dynamic portfolio optimization problems in the presence of general transaction costs, liquidity costs and market impact. This method extends the classical least squares Monte Carlo algorithm to incorporate switching costs, corresponding to transaction costs and transient liquidity costs, as well as multiple endogenous state variables, namely the portfolio value and the asset prices subject to permanent market impact. To handle endogenous state variables, we adapt a control randomization approach to portfolio optimization problems and further improve the numerical accuracy of this technique for the case of discrete controls. We validate our modified numerical method by solving a realistic cash-and-stock portfolio with a power-law liquidity model. We identify the certainty equivalent losses associated with ignoring liquidity effects, and illustrate how our dynamic optimization method protects the investor's capital under illiquid market conditions. Lastly, we analyze, under different liquidity conditions, the sensitivities of certainty equivalent returns and optimal allocations with respect to trading volume, stock price volatility, initial investment amount, risk aversion level and investment horizon.     

Exit as governance: do blockholders affect corporate innovation in large US firms?

Using a sample of large US firms, this study shows that blockholders in combination with liquidity can contribute positively to innovation (R&D investments). We contribute to the literature on managerial myopia that has focused mainly on large owners and their type (short-term/long-term). Our results are in line with recent theoretical studies arguing that blockholders in combination with market liquidity can mitigate managerial myopia, as suggested by the exit model of governance. The results indicate that blockholders can be efficient in resolving agency problems and that they can enhance long-term prosperity, even when the individual blockholder investment horizon may not be long.     

Nonlinear prediction of exchange rates with monetary fundamentals

This paper employs a neural network (NN) to study the nonlinear predictability of exchange rates for four currencies at the 1-, 6- and 12-month forecast horizons. We find that our neural network model with market fundamentals cannot beat the random walk (RW) in out-of-sample forecast accuracy, although it occasionally shows a limited market-timing ability. The neural network model without monetary fundamentals forecasts somewhat better for the British pound and the Canadian dollar. The model also exhibits some market-timing ability for the Deutsche mark at the 6- and 12-month horizons, and for the Canadian dollar at the 1-month horizon. In general, the model performs more poorly when it becomes more complex or when the forecast horizon lengthens. Our overall results are more on the negative side and suggest that neither nonlinearity nor market fundamentals appear to be very important in improving exchange rate forecast for the chosen horizons.     

Optimal market making in the presence of latency

This paper studies optimal market making for large-tick assets in the presence of latency. We consider a random walk model for the asset price and formulate the market maker's optimization problem using Markov Decision Processes (MDP). We characterize the value of an order and show that it plays the role of one-period reward in the MDP model. Based on this characterization, we provide explicit criteria for assessing the profitability of market making when there is latency. Under our model, we show that a market maker can earn a positive expected profit if there are sufficient uninformed market orders hitting the market maker's limit orders compared with the rate of price jumps, and the trading horizon is sufficiently long. In addition, our theoretical and numerical results suggest that latency can be an additional source of risk and latency impacts negatively the performance of market makers.     

Manipulation in market order models

We analyze a dynamic market order model similar to Kyle (Econometrica 53 (1985) 1315). We show that when the market faces uncertainty about the existence of the insider in the market, the equilibrium outcome changes in a significant way. In particular, the insider manipulates (i.e., trades in the wrong direction and undertakes short term losses) in every equilibrium, given a long enough horizon, and independently of the precise nature of noise trading in the market.     

An almost Markovian LIBOR market model calibrated to caps and swaptions

A new variant of the LIBOR market model is implemented and calibrated simultaneously to both at-the-money and out-of-the-money caps and swaptions. This model is a two-factor version of a new class of the almost Markovian LIBOR market models with properties long sought after: (i) the almost Markovian parameterization of the LIBOR market model volatility functions is unique and asymptotically exact in the limit of a short time horizon up to a few years, (ii) only minimum plausible assumptions are required to derive the implemented volatility parameterization, (iii) the calibration yields very good results, (iv) the calibration is almost immediate, (v) the implemented LIBOR market model has a related short-rate model. Numerical results for the two-factor case show that the volatility functions for the LIBOR market model can be imported into its short-rate model cousin without adjustment.     

Dynamic asset–liability management in a Markov market with stochastic cash flows

This paper provides a general model to investigate an asset–liability management (ALM) problem in a Markov regime-switching market in a multi-period mean–variance (M–V) framework. Emphasis is placed on the stochastic cash flows in both wealth and liability dynamic processes, and the optimal investment and liquidity management strategies in achieving the M–V bi-objective of terminal surplus are evaluated. In this model, not only the asset returns and liability returns, but also the cash flows depend on the stochastic market states, which are assumed to follow a discrete-time Markov chain. Adopting the dynamic programming approach, the matrix theory and the Lagrange dual principle, we obtain closed-form expressions for the efficient investment strategy. Our proposed model is examined through empirical studies of a defined contribution pension fund. In-sample results show that, given the same risk level, an ALM investor (a) starting in a bear market can expect a higher return compared to beginning in a bull market and (b) has a lower expected return when there are major cash flow problems. The effects of the investment horizon and state-switching probability on the efficient frontier are also discussed. Out-of-sample analyses show the dynamic optimal liquidity management process. An ALM investor using our model can achieve his or her surplus objective in advance and with a minimum variance close to zero.     

Modelling Time‐Variation in the Stock Return‐Dividend Yield Predictive Equation

Using data for forty markets, this paper examines the nature and possible causes of time‐variation within the stock return‐dividend yield predictive regression. The results in this paper show that there is significant time‐variation in the predictive equation for returns and that such variation is linked to economic and market factors. Furthermore, the strength and nature of those links are themselves time‐varying. The inclusion of this time‐variation in the predictive equation increases the predictive power compared to the standard constant parameter predictive model. Evidence is also reported for time‐varying dividend growth predictability. Long‐horizon predictability is also examined with evidence reported that the nature of the factors affecting time‐varying predictability changes with horizon. The results here, while directly contributing to the returns predictability debate, in particular regarding its existence and source, may also inform the discussion that links time‐varying expected returns (and risk premium) to economic factors.     

Term premium dynamics in an emerging market: Risk,liquidity, and behavioral factors

This paper contributes to the fixed income research by identifying determinants of term premium in an emerging market’s treasury bond yields with particular attention on ambiguity. We use Nelson–Siegel yield curves generated from daily bond price quotes as input to construct a three-factor affine term structure model which decomposes observed yields into risk-neutral and term premium components. We also construct an ambiguity index using intraday FX return data following Brenner and Izhakian (2018). Our analyses suggest that a combination of factors representing market risk, credit risk, liquidity, ambiguity, and investor sentiments can explain majority of the variation in term premia. Explanatory power of credit risk measures are found to increase while those of volatility, ambiguity, and sentiment measures diminish with the maturity horizon. The results imply that ambiguity aversion of bond investors is a major determinant of the shape of the yield curve as it drives the premia for short end of the yield curve lower in line with the expectation of flight-to-safety behavior.     

Consumption in incomplete markets

We develop a method to find approximate solutions, and their accuracy, to consumption–investment problems with isoelastic preferences and infinite horizon, in incomplete markets where state variables follow a multivariate diffusion. We construct upper and lower contractions; these are fictitious complete markets in which state variables are fully hedgeable, but their dynamics is distorted. Such contractions yield pointwise upper and lower bounds for both the value function and the optimal consumption of the original incomplete market, and their optimal policies are explicit in typical models. Approximate consumption–investment policies coincide with the optimal one if the market is complete or utility is logarithmic.     

Modelling mean reversion of asset prices towards their fundamental value

In this paper we extend the study of mean reversion behavior by modelling the fundamental value as a stochastic process. The market value of the asset is then modelled as a mean reverting Ornstein Uhlenbeck process towards the fundamental value. Solving backwards, we determine the functional form of the regression equation of changes in asset prices and returns to changes to the fundamental value. Using earnings and dividends as proxies for the fundamental value we test our model empirically. In general, other than the shortest horizon of 1-year, our model shows good explanatory power. Since our model is compatible with Campbell and Shiller (1988) framework in the earnings case and Fama and French (1988) model in the dividend case, the performance of our model has been compared with those two models. In comparison, the performance of our model is comparable to that of Campbell and Shiller and compares favorably with Fama and French.     

Impact of financial market uncertainty and macroeconomic factors on stock–bond correlation in emerging markets

This paper examines the impact of global financial market uncertainty and domestic macroeconomic factors on stock–bond correlation in emerging markets. In particular, by applying the wavelet analysis approach, we are able to examine stock–bond correlations over different time horizons in ten emerging markets. We find that stock–bond correlation patterns vary significantly between the time horizons. In particular, the correlation in short horizon changes the sign rapidly showing sustainable negative episodes while the correlation in long horizon stays positive most of the time. The most important factor influencing stock–bond correlation in short horizon is the monetary policy stance, while the factors with the greatest long-term impact are inflation and stock market uncertainty. Finally, global stock market uncertainty plays a more significant role than global bond market uncertainty in explaining stock–bond correlations in emerging markets.     

Profitability of time series momentum

We propose a continuous-time heterogeneous agent model consisting of fundamental, momentum, and contrarian traders to explain the significant time series momentum. We show that the performance of momentum strategy is determined by both time horizon and the market dominance of momentum traders. Specifically, when momentum traders are more active in the market, momentum strategies with short (long) time horizons stabilize (destabilize) the market, and meanwhile the market under-reacts (over-reacts) in short-run (long-run). This provides profit opportunity for time series momentum strategies with short horizons and reversal with long horizons. When momentum traders are less active in the market, they always lose. The results provide an insight into the profitability of time series momentum documented in recent empirical studies.     

Short‐Horizon Return Predictability in International Equity Markets

This study measures the degree of short‐horizon return predictability of 50 international equity markets and examines how its variation is related to the indicators of equity market development. Two multiple‐horizon variance ratio tests are employed to measure the degree of return predictability. We find evidence that return predictability is negatively correlated with publicly available indicators of equity market development. Our cross‐sectional regression analysis shows that the per capita gross domestic product, market turnover, investor protection, and absence of short‐selling restrictions are correlated with cross‐market variations in return predictability.     

CEO decision horizon and firm performance: An empirical investigation

We investigate the effect of top managers' myopia on firms' market valuation. We devise a measure of expected CEO tenure as a proxy for the length of CEO decision horizon. After accounting for the endogenous nature of CEO horizon, our empirical tests show that shorter CEO horizon is associated with more agency costs, lower firm valuation and higher levels of information risk. The results are consistent with the notion that a short CEO decision horizon is indicative of preference for investments that offer relatively faster paybacks at the expense of long-term value creation.     

Intraday Value at Risk (IVaR) using tick-by-tick data with application to the Toronto Stock Exchange

This paper investigates the use of tick-by-tick data for intraday market risk measurement. We propose a method to compute an Intraday Value at Risk based on irregularly spaced high-frequency data and an intraday Monte Carlo simulation. A log-ACD–ARMA–EGARCH model is used to specify the joint density of the marked point process of durations and high-frequency returns. We apply our methodology to transaction data for three stocks actively traded on the Toronto Stock Exchange. Compared to traditional techniques applied to intraday data, our methodology has two main advantages. First, our risk measure has a higher informational content as it takes into account all observations. On the total risk measure, our method allows for distinguishing the effect of random trade durations from the effect of random returns, and for analyzing the interaction between these factors. Thus, we find that the information contained in the time between transactions is relevant to risk analysis, which is consistent with predictions from asymmetric-information models in the market microstructure literature. Second, once the model has been estimated, the IVaR can be computed by any trader for any time horizon based on the same information and with no need of sampling the data and estimating the model again when the horizon changes. Backtesting results show that our approach constitutes reliable means of measuring intraday risk for traders who are very active in the market.     

Stock market short-termism—an international perspective

This study uses a VAR approach with a time-varying risk premium to see if stock market investors are short-term oriented, placing too much weight on current and near cash flows. Using aggregate stock market data for Australia, Germany, Japan, USA and UK markets (1977–1999), we find the degree of persistent under-valuation of future cash flows is far higher in the UK relative to the other countries in our sample, and is market-wide. Also, over the period, US investors appear to have consistently underestimated long horizon cash flows in current stock market valuations relative to a rational valuation model.     

The Effect of Serial Dependence on Multiperiod Holding Period Return Performance

The impact of the investment time horizon on risk‐return properties of asset returns depends on the presence of serial correlation and higher order serial dependencies. We present a methodology for decomposing multiperiod holding period return covariance into serial and cross‐sectional components using a recursive multiplicative model that captures the effects of serial and cross‐sectional dependencies and their joint effects without requiring a distributional form assumption. Applying this model to historical monthly return series for commonly held financial assets and portfolios of assets, we investigate the significance of the investment time horizon, the existence and relevance of time diversification, the inflation‐hedging effectiveness of different assets, and the appropriateness of applying traditional capital market theory in a multiperiod framework.     

Asset market games of survival: a synthesis of evolutionary and dynamic games

The paper examines a game-theoretic model of a financial market in which asset prices are determined endogenously in terms of a short-run equilibrium. Investors use general, adaptive strategies (portfolio rules) depending on the exogenous states of the world and the observed history of the game. The main goal is to identify portfolio rules, allowing an investor to “survive,” i.e., to possess a positive, bounded away from zero, share of market wealth over an infinite time horizon. The model under consideration combines a strategic framework characteristic for stochastic dynamic games with an evolutionary solution concept (survival strategies), thereby linking two fundamental paradigms of game theory.