When overconfident traders meet feedback traders

In this paper, we develop a model in which overconfident market participants and rational speculators trade against trend-chasers. We exhibit the unique linear equilibrium and assess the quality of prices according to the proportion of the different types of agents. We highlight how speculative bubbles arise when a large number of traders adopt a trend-chasing behavior. We show that overconfident traders can obtain positive expected profits. In particular, over-confident traders can outperform rational traders. The positive feedback trading enhances the negative correlation between the back-to-back prices changes and the volatility of prices as well. We show that positive feedback traders destabilize prices more than their overconfident opponents. Generally, overconfidence increases the volatility of prices and worsens the market efficiency. But, we show that in the presence of positive feedback trading, overconfidence improves the market efficiency and dampens the excess volatility

Irrational Financial Markets

We analyze a model where irrational and rational traders exchange a risky asset with competitive market makers. Irrational traders misperceive the mean of prior information (optimistic/pessimistic bias), the variance of prior information (better/lower than average effect)and the variance of the noise in their private signal (overconfidence/underconfidence bias). When market makers are rational we obtain results identical to Kyle and Wang (1997). However if market makers are irrational, we obtain that moderately underconfident traders can outperform rational ones and that irrational market makers can fare better than rational ones. Lastly we find that extreme level of confidence implies high trading volume.Irrationality

DTLS Performance in Duty-Cycled Networks

The Datagram Transport Layer Security (DTLS) protocol is the IETF standard for securing the Internet of Things. The Constrained Application Protocol, ZigBee IP, and Lightweight Machine-to-Machine (LWM2M) mandate its use for securing application traffic. There has been much debate in both the standardization and research communities on the applicability of DTLS to constrained environments. The main concerns are the communication overhead and latency of the DTLS handshake, and the memory footprint of a DTLS implementation. This paper provides a thorough performance evaluation of DTLS in different duty-cycled networks through real-world experimentation, emulation and analysis. In particular, we measure the duration of the DTLS handshake when using three duty cycling link-layer protocols: preamble-sampling, the IEEE 802.15.4 beacon-enabled mode and the IEEE 802.15.4e Time Slotted Channel Hopping mode. The reported results demonstrate surprisingly poor performance of DTLS in radio duty-cycled networks. Because a DTLS client and a server exchange more than 10 signaling packets, the DTLS handshake takes between a handful of seconds and several tens of seconds, with similar results for different duty cycling protocols. Moreover, because of their limited memory, typical constrained nodes can only maintain 3-5 simultaneous DTLS sessions, which highlights the need for using DTLS parsimoniously.Comment: International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC - 2015), IEEE, IEEE, 2015, http://pimrc2015.eee.hku.hk/index.htm

Irrational Financial Markets

We analyze a model where irrational and rational informed traders, exchange a risky asset with competitive market makers. irrational traders misperceive the mean of prior information (optimistic/pessimistic bias), the variance of prior information (better/lower than average effect) and the variance of the noise in their private siganal (overconfidence/underconfidence bias). When market makers are rational we obtain results identical to kyle and wang (1997). However if market makers are irrational, we obtain that moderately underconfident traders can outperform rational ones and that irrational market makers can fare better than rational ones. Lastly we find that extreme level of confidence implies high trading volume

When Overconfident Traders Meet Feedback Traders

We develop a model in which informed overconfident market participants and informed rational speculators trade against trend-chasers. In this model positive feedback traders act as Computer Based Trading (CBT) and lead to positive feedback loops. In line with empirical findings we find a positive relationship between the volatility of prices and the size of the price reversal. The presence of positive feedback traders leads to a higher degree of trading activity by both types of informed traders. Overconfidence can lead to less price volatility and more efficient prices. Moreover, overconfident traders may be better off than their rational counterparts

Heterogeneous Noisy Beliefs and Dynamic Competition in Financial Markets

This paper analyzes the competition of heterogeneously informed traders in a multi-auction setting. We obtain that the competition can take different forms depending on the number of traders, trading rounds and the noise in the information. When the number of traders is small and the number of trading rounds is large, traders may trade very aggressively at the opening and at the end of the trading day with lower trading intensity in between. Hence, we can explain volume patterns by the nature of the competition between traders rather than by pattern in the level of liquidity. We find that the noise in the signal may be beneficial for traders when the competition is strong as it gives them a monopolistic position on their private information. The amount of noise maximizing the trader’s expected profit increases with the number of trading rounds as well as the number of traders. This implies that the value of information is closely related to the market where that information is subsequently being used

When Overconfident Traders Meet Feedback Traders

We develop a model in which informed overconfident market participants and informed rational speculators trade against trend-chasers. In this model positive feedback traders act as Computer Based Trading (CBT) and lead to positive feedback loops. In line with empirical findings we find a positive relationship between the volatility of prices and the size of the price reversal. The presence of positive feedback traders leads to a higher degree of trading activity by both types of informed traders. Overconfidence can lead to less price volatility and more efficient prices. Moreover, overconfident traders may be better off than their rational counterparts