Does Gold Act as a Hedge or a Safe Haven? Evidence from Pakistan

This paper seeks to determine whether in Pakistan gold protects investors against the risks associated with the exchange rate, oil shocks, and stock returns by testing the hedging and safe haven properties of gold returns for the period from August 1997 to May 2016. The analysis has been done to understand the relationship between moderate (normal) and extremely tumultuous conditions through least squares and DCC-GARCH models. The key results indicate that gold acts as a hedge against exchange rate risk only whereas it acts as a safe haven in terms of the risks associated with the oil, exchange rate and stock market shocks—thereby indicating that investors can potentially invest in gold to hedge against losses emanating from the exchange rate, while they may avoid potential losses originating from turmoil conditions in terms of the exchange rate, oil, and stock markets. JEL Classification: E32, F31. Keywords: Gold Returns, Safe Haven, Hedge, DCC GARC

Pattern recognition techniques and financial analysis : a thesis presented in fulfilment of the requirements for the degree of Doctor of Philosophy in Finance at Massey University, Palmerston North, New Zealand

Listed in 2015 Dean's List of Exceptional ThesesThe balance sheet statement is an essential feature of financial reporting, and is expected to convey complete information on firms’ operating business decisions. Since these decisions are based on the manager’s perception of the existing and future investment opportunities, they cannot be directly observed. This results in two major data analysis issues. First, it is difficult to observe directly the most common operating business decisions; secondly, these decisions may not have a same linear relation to all firms and all firm’s performance measures. This thesis attempts to address these issues in three interconnected essays. The first essay examines an outcome of the double-entry bookkeeping system when financial transactions simultaneously shift a firm’s financial position, providing the special information to interpret the meaning of a transaction. Using the factor analysis model, this essay makes use of this information, and identifies the five fundamental factors (decisions) that can capture a firm’s time-varying operating business status in a given year. These factors include: financial flexibility, short-term credit, long-term investment, convertible debt usage, and preferred stock usage. The method of extracting these factors controls for missing variable bias, account for limited attention, and provide true decomposition of accounting aggregates such as total asset growth. These factors subsist in predicting future stock returns, forecasting a firm’s value (Tobin’s Q), cash flows, and earnings beyond their well-known determinants. The second essay explores the sources of return predictability contained in financial flexibility, which is the first factor identified in essay one. The horse races of the asset pricing versus mispricing tests find a significant positive premium on financial flexibility based return factor, and make it a candidate for a new priced factor. The evidence suggests that covariances dominate the characteristics, and it is non-redundant to well-established risk factors. This factor meets the new conservative minimum of t-statistics value of above 3.0 and is constructed using unobserved information. The final essay addresses the second issue in the data analysis by employing the nonlinear firm grouping technique – the K-means clustering analysis method. Firms are grouped in their 12 natural groups using the five fundamental factors identified in the first essay, and firm size as the clustering criteria. This essay shows how firms differ on priority and the composition of their common operating decisions. This type of firm grouping suggests that operating business decisions are related to firm-specific health and structure instead of industry. This essay recommends the nonlinear firm grouping prior to employing the linear regression models in predicting future performance measures to improve the precision of business analysis

Do sentiment indices impact the premium of prominent pricing factors?

This study investigates whether Google Search Volume Indices (GSVIs) bring shifts in the expected return of prominent pricing factors in comparison to the Volatility Index (VIX). The results show that compared to VIX, GSVIs bring less significant changes in expected premium on Fama–French’s five-factors and q-factors. Pessimistic sentiment indices (Market Crash and Bear Market) predict more significant variation in the premium of prominent pricing factors than optimistic sentiment indices (Market Rally and Bull Market), and also have a significant correlation with VIX representing downside risk. Furthermore, the sentiment indices are better in predicting premium on five-factors than q-factors

Is financial flexibility a priced factor in the stock market?

This paper develops a factor analysis-based measure for shifts in corporate financial flexibility (FFLEX) that can be observed from public accounting information. Companies that experience positive shifts in FFLEX are associated with higher future investment growth opportunities. We show that FFLEX is a robust determinant of future stock returns. Firms that have increased their financial flexibility are associated with lower stock returns in the subsequent period. A zero-cost return portfolio produces a significant positive monthly premium of 0.69%, which is driven by covariance (risk). Risk inherent in the flexibility factor is not explained away by either prominent pricing characteristics or factors