US Real Estate Inflation Prediction: Exchange Rates and Net Foreign Assets
Abstract: The global financial crisis of the 2007-2008 had a particular serve impact on the world economy, particularly international trade. The cause of this crisis has been linked to the housing price bubble and the inability of forecasters to predict the bubble burst. These forecasters relied mostly on time series models only using domestic predictors. In contrast, the international finance theory has found a role for international variables in determining US housing prices. Particularly under floating exchange rate regimes, the Dornbusch model predicts shocks to domestic or foreign economies will be reflected in exchange rates. When exchange rates are fixed, shocks are likely to affect the net foreign asset holdings, including housing. Further, Ferrero (2015) finds that deregulation in credit markets, accommodative US monetary policy, and fixed exchange rates have caused US housing prices and balance of payment measures (Current Account deficits and Net Financial Accounts) to be highly correlated. In this study, I combine components from both the empirical forecasting literature and international finance theory to develop a ordinary least squares forecasting model using measures of net financial accounts as predictors. Specifically, I conduct in-sample and out-of-sample forecasting accuracy tests of nested models containing both past housing price returns and the change in the change in net financial accounts over 1,2,and 4 quarter forecast horizons. I find that by including net financial accounts as predictors, forecasts of US housing price returns are more accurate by up to 40 percent. More specifically graphs of housing price forecast squared forecasting errors show that the largest gain from the net change in financial accounts is found during the housing bubble burst.
Stock Returns and Investor Sentiment: Textual Analysis and Social Media (with Adam Nowak)
Abstract: In general there is two conflicting modern finance theories regarding the pricing of assets, efficient market hypothesis and behavioral finance theory. The efficient market hypothesis states that due to rational investors a panel of stock returns should only be a function of market returns. In contrast, behavior finance theory states that due to irrational investors, and imperfect information, the cross section of stock returns are not only a function of market returns but also of investor sentiment, the prevailing mood or feelings of investors. Given this the empirical finance literature has shown that market investor sentiment, the overall feeling of investors towards the market, is an important determinant can be used to predict monthly and quarterly equity returns.Due to recent advances in big data analytics, higher frequency equity-specific investor sentiment measures can be constructed from social media posts. In this study, we construct a daily, equity-specific, investor sentiment indexes from Twitter. We use Bayesian methods, particularly the discrete choice inverse regression to build the dictionary of relevant words and phrases for construction of the indexes. We test the efficient market hypothesis by using a dynamic panel regression to determine the relationship between investor sentiment and stock returns. We further use in-sample and out-of-sample forecasting methods to determine the ability of our investor sentiment measures to forecast future returns. We find that our investor sentiment measure has a positive and statistically significant effect on individual stock returns. These findings are robust to different models and specifications.
Net Financial Accounts and US Real Estate Inflation
Abstract: Over the last decade, there has been a high correlation between balance of payment measures (Current Account deficits and Net Financial Accounts). The international finance theory has focused on determining the cause of this relationship. Specifically, this theory has found that deregulation in credit markets, accommodative US monetary policy, and fixed exchange rates caused US housing prices and balance of payments measures to move together. In 2015, the US Bureau of Economic Analysis released new estimates of balance of payments measures in line with international standards, such that now bilateral financial account data has been created. In this study, I use a number of components from bilateral financial account data, to forecast US housing prices. Further, to empirically test the implications of the international finance theory, I use dynamic factor analysis methods to create an bilateral financial account index to forecast US housing prices. I use in-sample and out-of-sample testing for forecast model validation and I find that many of these measures are able to produce improved forecasts of up to 50 percent.
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