Gal Zahavi

Loss restlessness and gain calmness: durable effects of losses and gains on choice switching

While the traditional conceptualization of the effect of losses focuses on bias in the subjective weight of losses compared with respective gains, some accounts suggest more global task-related effects of losses. Based on a recent attentional theory, we predicted a positive after-effect of losses on choice switching in later tasks. In two experimental studies, we found increased choice switching rates in tasks with losses compared to tasks with no losses. Additionally, this heightened shifting behavior was maintained in subsequent tasks that do not include losses, a phenomenon we refer to as “loss restlessness.” Conversely, gains were found to have an opposite “calming” effect on choice switching. Surprisingly, the loss restlessness phenomenon was observed following an all-losses payoff regime but not after a task with symmetric mixed gains and losses. This suggests that the unresolved mental account following an all-losses regime increases search behavior. Potential implications to macro level phenomena, such as the leverage effect, are discussed.

Association between Stock Market Gains and Losses and Google Searches

Experimental studies in the area of Psychology and Behavioral Economics have suggested that people change their search pattern in response to positive and negative events. Using Internet search data provided by Google, we investigated the relationship between stock-specific events and related Google searches. We studied daily data from 13 stocks from the Dow-Jones and NASDAQ100 indices, over a period of 4 trading years. Focusing on periods in which stocks were extensively searched (Intensive Search Periods), we found a correlation between the magnitude of stock returns at the beginning of the period and the volume, peak, and duration of search generated during the period. This relation between magnitudes of stock returns and subsequent searches was considerably magnified in periods following negative stock returns. Yet, we did not find that intensive search periods following losses were associated with more Google searches than periods following gains. Thus, rather than increasing search, losses improved the fit between people’s search behavior and the extent of real-world events triggering the search. The findings demonstrate the robustness of the attentional effect of losses.

On the relation between economic bubbles and effort gaps between sellers and buyers: An experimental study

Economic bubbles are an empirical puzzle because they do not readily fit the notion of an efficient market. We argue that bubbles are associated with a conflict and a gap in the allocation of effort during negotiation by sellers and buyers. We examined 21 experimental asset markets where in one condition players could buy and sell and in the other they could either buy or sell. The results indicated that when making concurrent buying and selling decisions the mean number of asks for sellers was 71% higher than the number of bids for buyers. Similar findings emerge in a re-analysis of data from Lei et al. (2001). Importantly, bubbles only emerged in markets where the number of asks was larger than that of bids. These findings indicate that bubbles are associated with increased negotiation effort when acting as a seller and diminished effort when acting as a buyer.

Nonlinear time series temperature modeling based on normal form

In this work nonlinear modeling of surface temperature is performed. To this end, we derive a simple physical model based on the reduction of Navier-Stokes-Boussinesq equations in rotating coordinates. The model is fit against low-pass embedding filtered real data from 10 stations in Europe and the near East. Using a learning set of 14000 points and forecasting 300 points forward, we obtain a good fit for 300 days ahead. Two key results are the reduction of a weather model to a six-dimensional center manifold that captures the main dynamics, and demonstration of nonlinearity in the time series is established through embedology.

Weather Derivative Pricing with Nonlinear Weather Forecasting

In recent years we witnessed a rapid growth of weather derivatives market. These derivatives are used to hedge energy contracts and distribute weather risk. While most derivative markets are complete and contingent climes replications are standard procedure, this special market is incomplete, and therefore modeling the weather is a more appropriate approach to pricing. In this work we base our modeling on a widely accepted physical approach, we use Navier-Stokes equations applied to a thin atmosphere as presented by Lorentz 1962. This modeling is considered by meteorologists a “very-long-weather” prediction, allows for an accurate and robust temperature forecasting. We show that under this setting we empirically outperform the standard approach to weather derivative pricing.

Online Learning of Informed Market Making

Many economic markets, including most major stock exchanges, employ market-makers to aid in the transactions and provide a better quality market. This Study is aimed to establish an analytical foundation for electronic market making strategy, by giving a probabilistic interpretation to the Bid-Ask spread. The suggested strategy will be optimized with on-line learning from the high frequency data of the TASE (Tel Aviv Stock Exchange) order book. Based on this foundation, we wish to create an automated securities dealer that will perform the task of providing liquidity to the markets efficiently, and with low downturn risk. We compare the expected performance of the automated dealer with several bench mark measures of Market liquidity such as those presented in Roll (1984) and Glosten & Milgrom (1985).