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As financial markets grow increasingly complex, traditional stock selection models often struggle to capture the intricate interplay between macroeconomic conditions and individual stock performance. In our latest paper, we explore a breakthrough approach— leveraging neural networks within a learning-to-rank framework — to enhance stock selection accuracy.

Once written off as dead money, Chinese banks have staged a quiet comeback—emerging as some of the market’s most resilient income plays. As China battles economic headwinds and investor confidence wavers, these state-backed giants are delivering strong dividends and unexpected stability. With limited alternatives, local investors are piling in, fuelling a trend that offshore sceptics never saw coming.

Income investing has long been of interest to equity investors seeking capital growth and attractive yields. Ageing populations and historically low interest rates have increased demand for such investments further. But income investing is not without its complexities. How can artificial intelligence help asset managers overcome these potential pitfalls and deliver consistent outperformance for clients?

After a horrendous 2022, where equities and bonds were hammered and the only safe haven appeared to be oil producers and commodities, 2023 has seen a revival of optimism in developed equity markets, while emerging markets have continued to suffer from the sluggish environment in China.

With rates at their highest for the past two decades, and fixed income allocation at the lowest due to the bloodbath for duration in 2022, many pension plans intend to reduce their equity allocation to collect the highest coupons they’ve seen for a long time. In this paper, we argue that all equities should not be treated equally in that reallocation move – while rebuilding a material fixed income exposure makes sense, some equity strategies should not be used to fund this reallocation, namely low volatility equities.