Robo Advisors, Algorithmic Trading and Investment Management: Wonders of Fourth Industrial Revolution in Financial Markets

Table 1

Table 3

Technological Forecasting & Social Change xxx (xxxx) xxx

Value weighted returns.

S&P500	10%
DJIA	13%
Nasdaq	12,50%
Equity Funds	17%
Fixed Income	7,58%
Money Market	4,64%
Hybrid Funds	8,24%
Robos	18,50%

income funds earned a 7.58% returns, while the hybrid funds have posted a decent 8.24% return rate. The interesting comparison here is between the equity and robo funds. The generally bullish markets have resulted in a 17% return for the equity funds, which is quite under- standable. However, for the robo funds, an average return of 18.5% reflects better investment picking. This is remarkable, as the composi- tion of the robos is not based on 100% equity instruments, and therefore reflect positively on the performance of the robo advisors.

The comparison on the basis of the adjusted Sharpe ratios, across the sample period, are given in Table 2. Our results demonstrate that robo advisors outperform all the other funds, as well as the stock market indices from the years 2016 to 2019. Moreover, the Sharpe ratio ranges from 0.81 to 2.182 for the robo funds. In 2016, the second ranked op- tions happen to be the money market funds, followed by the fixed in- come, and this trend has remained stable throughout the four years. We believe that a plausible reason for this is that, during periods of uncer- tainty, investors generally tend to park their funds in safer options. This notion is contradictory to that of Kacperczyk and Schnabl (2013), who suggested that the money market funds are not safe. We believe this is more relatable to the sample period taken into consideration, which is around the time of the financial crisis, when the confidence in banking and non-banking financial institutions was very uncertain and low. In 2019, the performance of the hybrid funds had been very close to the robos, which can be attributable to their ability of gaging the market timing (Comer, 2006). Overall, the Sharpe ratio demonstrates the valuable asset allocation, and the portfolio optimization by the robos. Interestingly, these findings are similar to those of Beketov et al. (2018). The results based on the reward to value at risk ratio are presented in Table 3. The findings are consistent with that of the adjusted Sharpe ratio. The ratio has reached a maximum value for the robo advisory funds, ranging from 1.15 in 2016, to 1.62 in 2019. As this measure compares the returns with extreme possible losses, a superior perfor- mance represents an optimized investment style for these automated investment funds. For other types of funds, the hybrid investments also demonstrate a robust performance on the basis of the reward to value at

risk variable.

The findings for Jensen’s Alpha, using the CAPM, Fama and French Model, and the Carhart Momentum Model are presented in Tables 4, 5 and 6. The results demonstrate a good fit for all three models, and their factor loadings. It is evident that during the year 2016 to 2019, the markets systematically priced the size, value and the momentum factors. Thus, the alpha has been positive across all the funds, and also the three specifications that have been taken into consideration. The indices
Table 2

Adjusted Sharpe ratio.

	2016	2017	2018	2019
S&P500	0,201	0,176	0,147	0,161
DJIA	0,182	0,245	0,187	0,363
Nasdaq	0,017	0,022	0,011	0,093
Equity Funds	0,254	0,460	0,364	0,580
Fixed Income	0,406	0,767	0,346	0,622
Money Market	0,460	0,280	0,400	0,315
Hybrid Funds	0,094	0,559	0,388	0,715
Robos	2182	1495	0,811	1136

Reward to value at risk ratio.

	2016	2017	2018	2019
S&P500	0,051	0,230	0,170	0,045
DJIA	0,123	0,062	0,064	0,437
Nasdaq	0,200	0,139	0,097	0,312
Equity Funds	0,199	0,380	0,002	0,139
Fixed Income	0,037	0,594	0,118	0,175
Money Market	0,309	0,242	0,254	0,276
Hybrid Funds	0,159	0,565	0,397	0,827
Robos	1150	1397	1674	1621

positive alpha demonstrates moderate returns for the passive strategies. Moreover, a surprising observation pertains to the alpha for the equity funds in the momentum specification. The observed alpha for the equity funds was 0.13%, compared to 0.87% for the NASDAQ, 0.98% for the DJIA, and 0.78% for the S&P500. Furthermore, the Hybird funds are seen to be marginally better, with an alpha of 0.77%, compared to the fixed income, with an observed alpha of 0.71%. The slight increase in the base rates during our sample period has resulted in decreasing the NAVs for the fixed income securities that could possibly explain the marginally low value of the alpha.

The results show a significant alpha for the robo advisory funds, across the three model specifications. Therefore, the CAPM based alpha is 0.9%, the size and value premium based is 1.0%, while for the four factor model, the observed alpha is computed to be 1.1%. All these es- timates are significant at a 99% level. As most of the robo advisors have a screening philosophy that includes value, growth and momentum, therefore, we see a tilt of these premiums, when it comes to explaining the performance of these automated funds. We note that the perfor- mance, as observed through the alpha estimation, is far superior to the alternate investment funds and the indices that we have considered in this study.

It is noteworthy that these findings are very encouraging for the automated advisory platforms. While the robo advisors offer a cost advantage in terms of their loadings, our results demonstrate that their performance is also superior, as compared to their larger and more diversified counterparts from the conventional fund segment. The su- perior performance of the robo advisors stems from the automation in the data analysis, and the quantitative tools that lead to a better in- vestment screening, and is also needed for the portfolio optimization. Also, the clients’ portfolios are created on the basis of the mean variance optimality. The rebalancing of the portfolios is automatic, in order to maintain the target asset allocation. This also results in providing tax friendly solutions through loss harvesting. The absence of human intervention also minimizes the possibility of conflict of interest, and agency problem between the investors and the fund managers. We believe that the resilient performance of these robo advisors, even during periods of high volatility, should increase the depth of the services.

4. Conclusion and policy suggestions

The technological innovations have resulted in many disruptions in the financial services industry, and fund management is also one of them. In the last five years, we have seen rapid evolution of fully automated investment management funds, known as the robo advisors. These virtual advisors offer many benefits to the investors, over their conventional counterparts. The primary factor here is the ease of access, and how many more retail investors can access financial advice through online platforms. The other distinguishing factor is the cost effective- ness, whereby the investors benefit from professional money manage- ment, and considerably low rates. However, despite the evolution of the robo advisors, there are limited studies that compare their performance with conventional funds, and therefore, this study attempted to fill that gap.

R. Tao et al.
Table 4

	S&P500	DJIA	Nasdaq		Equity Funds	Fixed Income	Money Market	Hybrid Funds	Robos
А	0,004 **	0,007	** 0,006	**	0,001 ***	0,007	*** 0,001	* 0,004	* 0,009	***

CAPM based funds performance.

Technological Forecasting & Social Change xxx (xxxx) xxx

	1981		2176		2232		3105		2944		1817		1706		2913
Вc	0,672	*	0,115	**	0,501	**	0,080	**	0,665	**	0,380	***	0,090	*	0,134	***
	1741		1982		2046		2145		2301		2851		1704		2872
Adj R^2	0,289		0,789		0,757		0,422		0,801		0,767		0,417		0,806

^*** represents significance at 99%,

^** at 95% and

^* at 90%, t statistics in Italics.
Table 5

	S&P500	DJIA	Nasdaq		Equity Funds	Fixed Income	Money Market	Hybrid Funds		Robos
А	0,0049 **	0,0096	* 0,0084	**	0,0008 **	0,0074 ***	0,0010	** 0,0059	**	0,0100	***

Funds performance Fama French factors model.

	2,3375		1,8619		2,4502		2,4483		3,5652		1,9372		1,9570		2,8320
Вc	0,4331	**	0,3440	*	0,1898	*	0,1286	**	0,7607	**	0,0202	**	0,7636	**	0,4849	**
	2,1080		1,8514		1,8445		2,0672		2,2940		2,3083		1,9342		2,4275
Вs	0,9076	**	0,3876	*	0,2075		0,6611	**	0,0542	*	0,0142		0,7062	**	0,4013	**
	2,0979		1,7886		0,7385		2,5360		1,8320		0,6284		2,4584		2,3925
Вh	0,6774	**	0,3928		0,8434	**	0,4045	**	0,8155	**	0,5303		0,0019	**	0,6085	**
	2,2525		0,7394		2,0351		2,3550		2,0857		0,7684		2,2652		2,3852
AdjR^2	0,2289		0,5344		0,1350		0,2041		0,4722		0,7658		0,3632		0,7910

^*** represents significance at 99%,

^** at 95% and

^* at 90%, t statistics in Italics.
Table 6

	S&P500	DJIA	Nasdaq	Equity Funds	Fixed Income	Money Market		Hybrid Funds	Robos
α	0,0078 **	0,0098	* 0,0087	* 0,0013	** 0,0071	* 0,0020	**	0,0077 ***	0,0108	***

Funds performance Carhart four factors model.

	2,0040		1,8961		1,8980		1,8461		1,6828		2,0209		2,8558		2,8793
βc	0,8540	**	0,2933	**	0,5762	*	0,6605	**	0,5568	**	0,9606	*	0,0029	**	0,1734	***
	2,2941		2,0958		1,7399		2,1656		2,3893		1,8004		2,1590		2,9590
βs	0,2002	**	0,7657	**	0,2058		0,0732	**	0,4213		0,2729		0,5547	**	0,8156	***
	2,4052		2,1172		0,2934		2,0566		0,9401		1,5771		1,9634		3,3477
βh	0,9227	**	0,7463	*	0,8435		0,7772	*	0,9714		0,1539		0,9334	**	0,4256	***
	1,9517		1,6835		1,2144		1,9086		1,3666		0,2464		2,2663		3,0747
βm	0,2062	**	0,5084	**	0,8065		0,1597	**	0,4994		0,8301		0,4844	**	0,2111	***
	1,7245		1,9945		0,3463		2,2385		0,1903		1,4433		2,3149		3,0573
AdjR^2	0,1796		0,4481		0,5150		0,3869		0,5779		0,2232		0,3477		0,7313

^*** represents significance at 99%,

^** at 95% and

^* at 90%, t statistics in Italics.
In this paper, we compare the performance of robo advisors with other mutual funds. The sample period spans from a time frame of 2016 to 2019, and weekly returns are considered for this purpose. Our find- ings from the risk to return ratios, and the performance alpha demon- strate that during this period, the robo advisors have outperformed other mutual funds, and also some major equity indices. These results remain robust for different specifications of Risk to Reward, and Capital Asset Pricing Model, Fama and French Size, the Value Factors Models, as well as the Momentum Based Extension. These findings have important im- plications, as they represent and reveal that robo advisors are not only easily accessible and cost effective, but also provide superior risk adjusted returns to the investors. Furthermore, the robo advisors do not have an emotional aspect that relates to investment decisions and in- vestments, and are free from any related biases. This ensures that in- vestors’ money does not suffer from the behavioral aspects that are linked to the financial advisors. There is, however, one possible limita- tion of our study. The robo advisory is a new phenomenon, and hence the available data is not very exhaustive. Therefore, it will be interesting to revisit these findings once a larger dataset is available. The recent Covid-19 has also provided an opportunity to assess the performance of

robo advisors, amidst financial turmoil, that could be explored in future studies.

CRediT authorship contribution statement

Ran Tao: Conceptualization, Data curation, Visualization, Investi- gation, Writing - review & editing. Chi-Wei Su: Conceptualization, Methodology, Software, Formal analysis, Supervision. Yidong Xiao: Software, Validation, Project administration, Writing - review & editing. Ke Dai: Conceptualization, Methodology, Validation, Visualization, Writing - review & editing. Fahad Khalid: Writing - review & editing.

Acknowledgement

This research is partly supported by the National Social Science Fund of China (20BJY021).

R. Tao et al.

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Dr Ran Tao major in Engineering and Applied Science and she is familiar with Statistical software and Data Analysis Methods. She majors in Finance and economic field and excellent in Time Series Analysis. She has already published more than 50 papers in SCIE and SSCI indexed journals.
Dr. Chi-Wei Su is a full professor at the School of Economics, Qingdao University. He majors in Finance and economic field and excellent in Time Series Analysis. He has already published more than 170 papers in SCIE and SSCI indexed journals.
YiDong Xiao is a graduate student at the Department of Finance, Isenberg School of Management, University of Massachusetts, Amherst, United States. Xiao interest in research on financial producs, financial intermediaries, and financial management. Xiao involves in a different research project.
Ke Dai is an undergraduate student at Business School, Hohai University, Nanjing, China. Dai majors in Finance and economic field and excellent in Time Series Analysis. Dai in- volves in a different research project.
Dr. Fahad Khalid is an Assistant Professor at School of Business, Guilin University of Electronic Technology, China. He received his doctorate degree in Business Management from University of International Business & Economics, China. His-research interests include sustainability management, carbon disclosure and strategy and institutions in China. He has published in top journals, i.e. Journal of Cleaner Production and Environ- mental Science and Pollution research.