Blockchain-Empowered Mobile Edge Intelligence, Machine Learning and Secure Data Sharing

Driven by recent advancements in machine learning, mobile edge computing (MEC) and the Internet of things (IoT), artificial intelligence (AI) has become an emerging technology. Traditional machine learning approaches require the training data to be collected and processed in centralized servers. With the advent of new decentralized machine learning approaches and mobile edge computing, the IoT on-device data training has now become possible. To realize AI at the edge of the network, IoT devices can offload training tasks to MEC servers. However, those distributed frameworks of edge intelligence also introduce some new challenges, such as user privacy and data security. To handle these problems, blockchain has been considered as a promising solution. As a distributed smart ledger, blockchain is renowned for high scalability, privacy-preserving, and decentralization. This technology is also featured with automated script execution and immutable data records in a trusted manner. In recent years, as quantum computers become more and more promising, blockchain is also facing potential threats from quantum algorithms. In this chapter, we provide an overview of the current state-of-the-art in these cutting-edge technologies by summarizing the available literature in the research field of blockchain-based MEC, machine learning, secure data sharing, and basic introduction of post-quantum blockchain. We also discuss the real-world use cases and outline the challenges of blockchain-empowered intelligence

Identifying Key Drivers of Return Reversal with Dynamical Bayesian Factor Graph

<div><p>In the stock market, return reversal occurs when investors sell overbought stocks and buy oversold stocks, reversing the stocks’ price trends. In this paper, we develop a new method to identify key drivers of return reversal by incorporating a comprehensive set of factors derived from different economic theories into one unified dynamical Bayesian factor graph. We then use the model to depict factor relationships and their dynamics, from which we make some interesting discoveries about the mechanism behind return reversals. Through extensive experiments on the US stock market, we conclude that among the various factors, the liquidity factors consistently emerge as key drivers of return reversal, which is in support of the theory of liquidity effect. Specifically, we find that stocks with high turnover rates or high Amihud illiquidity measures have a greater probability of experiencing return reversals. Apart from the consistent drivers, we find other drivers of return reversal that generally change from year to year, and they serve as important characteristics for evaluating the trends of stock returns. Besides, we also identify some seldom discussed yet enlightening inter-factor relationships, one of which shows that stocks in <b>Finance and Insurance</b> industry are more likely to have high Amihud illiquidity measures in comparison with those in other industries. These conclusions are robust for return reversals under different thresholds.</p></div

The probabilities of <i>IsReversal</i> = 1 for the other out-of-sample years.

<p>The probabilities of <i>IsReversal</i> = 1 for the other out-of-sample years.</p

The inference results for year 2007.

<p>The inference results for year 2007.</p

The <i>free</i> and highest conditional probabilities of <i>Illiquidity</i> = 1 given (<i>HighNear</i>, <i>LowNear</i>).

<p>The <i>free</i> and highest conditional probabilities of <i>Illiquidity</i> = 1 given (<i>HighNear</i>, <i>LowNear</i>).</p

The <i>free</i> and highest conditional probabilities of <i>Turnover</i> = 1 given <i>Industry</i>.

<p>The <i>free</i> and highest conditional probabilities of <i>Turnover</i> = 1 given <i>Industry</i>.</p

The probabilities of <i>IsReversal</i> = 1 for year 2011.

<p>The probabilities of <i>IsReversal</i> = 1 for year 2011.</p

The <i>free</i> and highest conditional probabilities of <i>Turnover</i> = 1 given (<i>HighNear</i>, <i>LowNear</i>).

<p>The <i>free</i> and highest conditional probabilities of <i>Turnover</i> = 1 given (<i>HighNear</i>, <i>LowNear</i>).</p

Evidence <i>e</i>_<i>i</i>.

<p>Evidence <i>e</i>_<i>i</i>.</p

The proportions of reversal instances under different values of <i>r</i>_<i>th</i>.

<p>As a <i>r</i>_<i>th</i> that is too small will lead to many random fluctuations among reversal instances, the value of the threshold should be carefully chosen. The figure can help decide what values of <i>r</i>_<i>th</i> should be experimented with.</p