Hedging Demand in Long-Term Asset Allocation with an Application to Carry Trade Strategies*

We derive a closed-form expression for the mean and marginal hedging demand on risky assets in long-term asset allocation problems for individuals with constant relative risk aversion preferences. Our parametric portfolio policy rule accommodates an arbitrarily large number of state variables for predicting the state of nature and number of assets in the portfolio. The closed-form expression for the hedging demand is exact under polynomial specifications of the portfolio policy rule and a suitable approximation for unknown smooth parametric portfolio policy rules using Taylor expansions. The hedging demand on risky assets depends positively on the predictability of the risky asset and the persistence of the predictors, and negatively on the degree of investor’s relative risk aversion. We illustrate these insights empirically for a basket of currencies by showing the outperformance of rebalancing carry trade strategies over different investment horizons against a short-term (myopic) portfolio.

Dynamic Attention Behavior Under Return Predictability

We investigate the dynamic problem of how much attention an investor should pay to news in order to learn about stock-return predictability and maximize expected lifetime utility. We show that the optimal amount of attention is U-shaped in the return predictor, increasing with both uncertainty and the magnitude of the predictive coefficient and decreasing with stock-return volatility. The optimal risky asset position exhibits a negative hedging demand that is hump shaped in the return predictor. Its magnitude is larger when uncertainty increases but smaller when stock-return volatility increases. We test and find empirical support for these theoretical predictions. This paper was accepted by Gustavo Manso, finance.

Technical Note—Options Portfolio Selection

We develop a new method to optimize portfolios of options in a market where European calls and puts are available with many exercise prices for each of several potentially correlated underlying assets. We identify the combination of asset-specific option payoffs that maximizes the Sharpe ratio of the overall portfolio: such payoffs form the unique solution to a system of integral equations, which reduces to a linear matrix equation under discrete representations of the underlying probabilities. Even when risk-neutral volatilities are all higher than physical volatilities, it can be optimal to sell options on some assets while buying options on other assets, for which the positive hedging demand outweighs negative demand stemming from asset-specific returns.

Prediction of Bitcoin using Recurrent Neural Network

For the past couple of years, Machine learning and trading helped by artificial intelligence has drawn growing interest. Here, the approach is used to test the hypothesis that the inefficiency of cryptocurrency industry can be exploited in order to produce anomalous revenue. For the duration between Nov. 2015 and Apr. 2018, daily data for 1, 681 crypto currencies were analyzed. Simple trade techniques supported by state-of -the-art machine learning algorithms are seen to outperform the traditional benchmarks. The results obtained imply that non-trivial, but fundamentally simple, algorithmic processes will help to predict the short-term future of the cryptocurrency market. The popularity of cryptocurrencies had skyrocketed in 2017 due to several consecutive months of super-exponential growth of market capitalization. There are over 1,500 currently recorded cryptocurrencies actively trading today with the cryptocurrencies sitting on more than $300 billion [2], and a total market capitalization of over $800 billion in January 2018. According to a recent survey, between 2.9 and 5.8 million privates as well as institutional investors are in the numerous investment networks and access to markets has become easier over time. In a number of online markets, major crypto currencies can be purchased using fiat currency, and then used in order to purchase less known crypto currencies. The average trading amount is globally exceeding $15bn. About 170 money market funds had been invested in cryptocurrencies since 2017, and Bitcoin futures are launched in order to satisfy the Bitcoin trading and hedging demand for the market. The main objective of the work is to predict the Bitcoin prices, one of the most popular and widely used cryptocurrency which is a source of attraction for many investors as a source of profit or investment. But the market for the cryptocurrencies been volatile since the day it was first introduced. So, the approach towards the survey is to use LSTM RNN and use the available dataset and train the model to give the highest possible accuracy and to provide a real-time price of the Bitcoin for the following days.

Payout Yields and Stock Return Predictability: How Important Is the Measure of Cash Flow?

We compare the stock return forecasting performance of alternative payout yields. The net payout yield produces more accurate forecasts relative to alternatives, including the traditional dividend yield. This remains true even after excluding several years during the Great Depression when issuance was unusually high. The measure of cash flow used to form the yield matters economically. Long-term investors’ hedging demand for stock is considerably reduced when net payout, rather than dividends, serves as the cash-flow measure. An agent relying on an incorrect payout measure is willing to pay an economically significant “management fee” to switch to the optimal policy.

LEARNING AND PORTFOLIO DECISIONS FOR CRRA INVESTORS

We maximize the expected utility from terminal wealth for a Constant Relative Risk Aversion (CRRA) investor when the market price of risk is an unobservable random variable and explore the effects of learning by comparing the optimal portfolio under partial observation with the corresponding myopic policy. In particular, we show that, for a market price of risk constant in sign, the ratio between the portfolio under partial observation and its myopic counterpart increases with respect to risk tolerance. As a consequence, the absolute value of the partial observation case is larger (smaller) than the myopic one if the investor is more (less) risk tolerant than the logarithmic investor. Moreover, our explicit computations enable to study in detail the so called hedging demand induced by parameter uncertainty.