Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data

The recent Nobel-prize-winning detections of gravitational waves from merging black holes and the subsequent detection of the collision of two neutron stars in coincidence with electromagnetic observations have inaugurated a new era of multimessenger astrophysics. To enhance the scope of this emergent field of science, we pioneered the use of deep learning with convolutional neural networks, that take time-series inputs, for rapid detection and characterization of gravitational wave signals. This approach, Deep Filtering, was initially demonstrated using simulated LIGO noise. In this article, we present the extension of Deep Filtering using real data from LIGO, for both detection and parameter estimation of gravitational waves from binary black hole mergers using continuous data streams from multiple LIGO detectors. We demonstrate for the first time that machine learning can detect and estimate the true parameters of real events observed by LIGO. Our results show that Deep Filtering achieves similar sensitivities and lower errors compared to matched-filtering while being far more computationally efficient and more resilient to glitches, allowing real-time processing of weak time-series signals in non-stationary non-Gaussian noise with minimal resources, and also enables the detection of new classes of gravitational wave sources that may go unnoticed with existing detection algorithms. This unified framework for data analysis is ideally suited to enable coincident detection campaigns of gravitational waves and their multimessenger counterparts in real-time.Comment: 6 pages, 7 figures; First application of deep learning to real LIGO events; Includes direct comparison against matched-filterin

Essays on the relationship between investor sentiment and Real Estate Investment Trusts

Real Estate Investment Trusts (REITs) are federal tax-exempt firms originated in 1960 to allow investors participation in professionally managed real estate to attain greater portfolio diversification. Although REITs are often considered transparent and informationally efficient, extant literature suggests that investor behavioral biases impact their prices and returns. This dissertation examines the relationship between investor sentiment and REITs, contributing to the literature in the following distinct ways. First, I examine the contemporaneous and intertemporal impact of changes in sentiment on REIT returns making a distinction between sentiment derived from large institutional investors and small individual investors. Results suggest that sentiment from both groups of investors positively affect REIT industry returns contemporaneously; however, no intertemporal effect is observed. Closer examination reveals that institutional investor sentiment has a larger impact on REIT returns than does individual sentiment, consistent with significant increases in institutional ownership after 1992. Second, I study the impact of the 2008-2009 REIT liquidity crisis on REIT industry returns and volatility and the role of investor sentiment during this period of market turmoil. Results indicate that the liquidity crisis negatively impacted REIT industry returns and significantly increased volatility. Findings suggest that sentiment is a significant factor in explaining REIT returns and volatility during the crisis, however, consistently larger coefficients for institutional sentiment imply dominance over individual investor sentiment. Liquidity constraints severely affected REIT industry outlooks during the crisis which pushed investors to adjust their portfolios, affecting returns negatively and pushing volatility upward. Third, I investigate the asymmetric effect of changes in investor sentiment on REIT industry returns and volatility. Results suggest an asymmetric impact between positive and negative changes in institutional investor sentiment on REIT returns and volatility; however, no asymmetric impact is observed for individual investor sentiment. After the REIT liquidity crisis, the sentiment-REIT relationship appears to change. Post-crisis, institutional investor sentiment does impact REIT returns significantly, whereas positive changes in individual investor sentiment positively affect returns. Post-crisis volatility appears to be positively influenced only by bullish changes in institutional sentiment while significantly affected by both negative and positive shifts in individual investor sentiment

The Impact of TARP Bailouts on Stock Market Volatility and Investor Fear

The Emergency Economic Stabilization Act of 2008 was the response of the Federal government to the economic crisis of 2007-2009. Within this act, the Troubled Asset Relief Program (TARP) was the mechanism to attempt to stabilize the financial market through the injection of liquidity into troubled firms. This paper examines the effect of TARP bailouts on stock market volatility and investor fear. Using an event study methodology, we find evidence of a significant decrease in stock-market volatility on the day of bailouts, and the day after. Additionally, findings show that the VIX, a proxy of investor fear, significantly declines on the second day subsequent to bailouts. The results suggest that government intervention, in the form of bailouts, is successful in stabilizing financial markets and reducing investor anxiety in the short-run

Changes in sentiment on REIT industry excess returns and volatility

REIT characteristics pose unique risks and benefits to investors who seek liquid diversification and hedging vehicles to complement their portfolios. This paper tests for the asymmetric effect of individual and institutional investor sentiment on REIT industry returns and conditional volatility. We simultaneously model the impact of two markedly different groups of investors on the return generating process of the REIT industry. Our findings suggest that noise trading imposes significant systemic risk on the realization of REIT industry returns. Interestingly, corrections in institutional investor expectations have a larger effect on REIT industry returns and volatility than changes in individual investor expectations. More specifically, bearish shifts in institutional investor expectations of future market conditions have a significantly larger impact on returns and volatility than bullish shifts. Results align with the overreaction to negative information and loss aversion hypotheses

The Liquidity Crisis, Investor Sentiment, and REIT Returns and Volatility

The real estate investment trust (REIT) industry experienced a liquidity crisis resulting from reduced access to credit commitments as banks were restoring their balance sheets during the 2007–2009 financial crisis. Employing generalized autoregressive conditional heteroscedasticity (GARCH) models, we examine the impact of the liquidity crisis and investor sentiment on REIT returns and volatility over the December 2001 to February 2013 period. We find that the liquidity crisis negatively impacts REIT returns and helps explain increases in volatility; this finding is robust to multiple specifications. We show that investor sentiment is a significant factor in the REIT return-generating process with institutional sentiment playing a dominating role over individual sentiment; furthermore, institutional sentiment was the only relevant sentiment variable during liquidity crisis