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algorithms to create more favorable output. Of course, this requires considerable amounts of computing power and it has not been until recent years where humans have closed the gap to creating amore sophisticated and developed form of AI.

2.7 AI Stock Trading

      So, let us make an effort to understand how does AI apply to the stock markets and stock trading? For a technology which specializes into number crunching and analyze that data to predict the future, AI is a natural fit for the world of finance. In stock market every day, a lot of data is generated regarding lows, highs, volume of trade, etc., which is used by the analysts to predict the future movements of stocks. A combination of deep learning along with machine learning allows financial firms to analyze not only stock price fluctuations but also unstructured data that reveals patterns of behavior that may have not been perceptible by a human [13]. This paves way for a new level of accuracy in trading decisions that goes beyond traditional investing strategies. Of course, these are just some of the known usages of AI. Stock markets around the world have realized the application of AI and begun to shift their focus toward bringing in AI experts from Information Technology sector to the world of hard core finance and investment. This competitive uproar has led to companies to move forward and apply this technology in relation to real-world investing applications.

2.8 Algorithmic (Algo Trading) Trading

      Algorithmic Trading or Algo Trading refers to triggering trades on stock exchanges based on predefined criteria and without any human interference using computer programs and software [14]. Algorithmic Trading is normally defined as the use of computer algorithms to mechanically make certain orders, trading decisions, and manage those orders after compliance. While being a division of algorithmic trading, high-frequency trading involves buying and selling oodles of shares in a very small period of time like fractions of seconds. After so many frauds and downturns in stock market, the common agreement is that algorithmic trading is an unavoidable evolution of the trading process and markets all over the world have implemented various measures to provide a unhampered experience to investors. In the United States and other such developed stock markets, High-Frequency Algorithmic trading accounts for about 70% of trades in equities segment. In India, this percentage of trades done with the help of algorithmic trading to the total turnover has moved up to as much as 49.8%.

      In India, Algo Trading was introduced on April 3, 2008, when DMA (Direct Market Access) facility was made available to institutional clients by SEBI (Securities and Exchange Board of India). DMA facility was a platform which allowed brokers to provide their set up to clients and gave them direct right to use to the exchange trading system without any participation of a broker. To start with retail, clients were not given this facility; thus, only institutional clients could avail this service. But later, it was also given to retail-individual traders. It brought down the costs of trading for the institutional investors and also helped in improved execution by reducing the time spent in steering the order to the broker and issuing the necessary commands. But DMA had a negative effect on the brokerage business of stock brokers as investors both institutional and retail clients start accessing DMA services. To sustain the times, they started providing computerized software to the clients.

2.9 Conclusion

Since 1800s, Indian stock markets have come a long way from trading in the ring to complete computer-based trading to algorithmic trading. These have been years of complete change and cumulative improvement. Programs which were used by select few are now approachable to people at large creating more volumes in the exchanges every day. Markets have given confidence to the retail traders with safety measures and transparency. The future defiantly lies in more sophisticated research tools on computers more advanced and AI featured.

References

      1. Strasburg, J. and Bray, C., Six charged in vast insider-trading ring. Wall St. J., A1, 2009.

      2. Khandelwal, A., Data Compliance in Stock Broking–Boon or Bane. Int. J. Res. Eng., IT Soc. Sci., VI, Issue IV, 28-33, 2015.

      3. Hendershott, T., Electronic trading in financial markets. IT Prof., 4, 10–14, 2003.

      4. Jennings, M., The Lessons from Galleon Hedge Fund and the Insider Trading Ring. Corp. Finance Rev., 14, 5, 43, 2010.

      5. Rahim, A., Problems and prospects of online share trading practices in India. Int. J. Marketing Financ. Serv. Manage. Res., 2, 4, 150-155, 2013.

      6. Rajendran, R. and Palanisamy, S., The History and Evolution of Indian Online Share Trading as a Technological Tool for Sustainable Wealth Creation for Individual Investors. Presented at International Conference on Innovations in Engineering and Technology for Sustainable Development, Bannari Amman Institute of Technology, 2012.

      7. Khandelwal, A., E-Accounting as a Way to Maintain Financial Data with Special Reference to Indian Stock Market. Available at SSRN 2859616, Economic Challenger, 73, 77-80, 2016.

      8. Kyriacou, K. and Mase, B., Rolling settlement and market liquidity. Appl. Econ., 32, 8, 1029–1036, 2000.

      9. J. Morik, C.V. Austin, E. Lunceford, B. Blank, U.S. Patent Application No. 13/894,991, 2013.

      10. Amsaveni, R. and Gomathi, S., A study on satisfaction of online share traders with special reference to Coimbatore district of Tamil Nadu in India. Asia-Pac. J. Manage. Res. Innov., 8, 2, 145–153, 2012.

      11. Stout, L.A., The investor confidence game. Brook. L. Rev., 68, 407, 2002.

      12. Srivastava, S., Impact of internet growth on the online stock trading in India. Available at SSRN 1964838, 2011.

      13. Barclay, M.J. and Hendershott, T., Price discovery and after trading hours. Rev. Financ. Stud., 16, 1041–1073, 2003.

      14. Hendershott, T., Jones, C.M., Menkveld, A.J., Does algorithmic trading improve liquidity? J. Finance, 66, 1, 1–33, 2011.

      Email: [email protected]; [email protected]

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      AI in HR a Fairy Tale of Combining People, Process, and Technology in Managing the Human Resource

       Jyoti Jain1 and Sachin Gupta2*

       1Department of Management, JECRC University, Jaipur, (Rajasthan), India

       2Department of Business Administration, Mohanlal Sukhadia University, Udaipur (Rajasthan), India

       Abstract

      It is the world of magical performance with unseen possibilities. It is the time of working smart rather than working hard. It is the time when machines in collaboration with the human are working hand to hand to ease the work and hence to increase the productivity. Use of machines with human help is not a new idea or a thought, way back with the use of simple computer till Hi-Fi gadgets like laptops, and employees are rather habitual of working with machines.

      The use of Artificial Intelligence is not limited to the Information Technology sector only but has laid its different wings toward the medical, education, business, automotive vehicles, etc. AI in HR has also enabled the managers to work efficiently starting from the smart people analytics to the team training and hiring; the AI in HR is also dealing with the data transactions and limiting the repetitive work and low value task.

      The present chapter focuses on the use of AI in HR as AI and HR are now going together to manage the human as the resource and are enabling the organizations to help out

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