I am going to talk regarding the Asset Management industry here in points.
1) Factor Investing and Strategic Asset Allocation: I am clubbing these two together because asset allocation now is largely influenced by factors on various levels. Factor investing isn’t as new as some fin-media might lead you to believe, Factor research has been in academia for years, but I believe the most significant application to investing was when Cliff Asness started AQR in 1998. But it did take off this decade and I’ll mention that research here. Over the years they have published some amazing white papers which you can read at https://www.aqr.com/Insights/Research . I believe the most defining paper on Factors in this decade would be “Value Momentum Everywhere” by AQR and NYU that cemented the existence of risk premia for these factors across the world and also the diversification effect of value and momentum together. Another amazing paper would be “MSCI Multi-Asset class factor model” , it broadly lays out the implementation and strategy for implementing asset allocation across the globe using various factors like inflation, value etc. across asset classes like Eq, FI, Private Eq , REITs etc. Another mention would be use of Carry factor across asset classes defined in papers like “Carry” by AQR.
2) Tactical Asset Allocation: I am separating the short-term allocation because the research here varies a lot, from use of Factors (BlackRock) to something like Deep Learning (Quantopian). While there is plenty of open research on Strategic Allocation, Tactical remains propriety for most part (You can find white papers for above 2 with a google search though). Unlike the first point I cannot point to “concrete research” in this part since it’s just not cemented enough. You can search for “Global Tactical Asset Allocation” on SSRN and you’ll find plenty of papers, try to implement them and see which works for you.
3) Machine Learning : Instead of me pointing out the plethora of research here, I’d suggest you read what I believe is one of the best books on the topic “Advances in Financial Machine Learning” by Marcos Lopez de Prado. I find the back-testing part in this books one of the most realistic, worth a read.