Abstract
With the advent of artificial intelligence, programs and procedures have streamlined the automated routine tasks and improved the customer service experience which helped businesses in their bottom line. The artificial intelligence world started exploding around 2000s, and it seemed that no industry or sector could resist its impact and prevalence by remaining untouched in these recent times. The world of quant finance and hedge funds is among those which did necessary ways to leverage the power of this ever-changing technology. The reinforcement recurrent learning (RRL) type of technique is being used to optimize different assets in the trading system and has reached outstanding results. Here, we use shares’ capabilities to locate basics which include ee-e book value, dividends, or sales. We attempt to research those hazard and go back traits of fundamentally weighted and market cap-weighted indexes and hire numerous hazard-adjusted techniques to make certain that those returns’ variations have been now no longer pushed with the aid of using hazard. To deal with this assignment of continuation of movement and operating in a multi-dimensional kingdom space, we proposed this stacked deep dynamic recurrent learning (SDDRL) structure to assemble a real-time ideal portfolio.
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Walia, G.S., Sinha, N., Kashyap, N., Kumar, D., Sahana, S., Das, S. (2022). Financial Modeling Using Deep Learning. In: Shaw, R.N., Das, S., Piuri, V., Bianchini, M. (eds) Advanced Computing and Intelligent Technologies. Lecture Notes in Electrical Engineering, vol 914. Springer, Singapore. https://doi.org/10.1007/978-981-19-2980-9_23
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DOI: https://doi.org/10.1007/978-981-19-2980-9_23
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